{
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zkufh760uvF3"
      },
      "source": [
        "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n",
        "\n",
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/Training/named_entity_recognition/NLU_training_NER_demo.ipynb)\n",
        "\n",
        "\n",
        "\n",
        "# Training a Named Entity Recognition (NER) model with NLU\n",
        "With the [NER_DL model](https://nlp.johnsnowlabs.com/docs/en/annotators#ner-dl-named-entity-recognition-deep-learning-annotator) from Spark NLP you can achieve State Of the Art results on any NER problem\n",
        "\n",
        "This notebook showcases the following features :\n",
        "\n",
        "- How to train the deep learning classifier\n",
        "- How to store a pipeline to disk\n",
        "- How to load the pipeline from disk (Enables NLU offline mode)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dur2drhW5Rvi"
      },
      "source": [
        "# 1. Colab Setup"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hFGnBCHavltY"
      },
      "source": [
        "# Install the johnsnowlabs library\n",
        "! pip install -q johnsnowlabs\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f4KkTfnR5Ugg"
      },
      "source": [
        "# 2. Download conll2003 dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OrVb5ZMvvrQD",
        "outputId": "3b928110-85ec-43b4-fb0b-59b7f8a1e82b"
      },
      "source": [
        "! wget https://github.com/patverga/torch-ner-nlp-from-scratch/raw/master/data/conll2003/eng.train"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-10-27 13:23:45--  https://github.com/patverga/torch-ner-nlp-from-scratch/raw/master/data/conll2003/eng.train\n",
            "Resolving github.com (github.com)... 140.82.112.4\n",
            "Connecting to github.com (github.com)|140.82.112.4|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://raw.githubusercontent.com/patverga/torch-ner-nlp-from-scratch/master/data/conll2003/eng.train [following]\n",
            "--2023-10-27 13:23:45--  https://raw.githubusercontent.com/patverga/torch-ner-nlp-from-scratch/master/data/conll2003/eng.train\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.109.133, 185.199.108.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 3283420 (3.1M) [text/plain]\n",
            "Saving to: ‘eng.train’\n",
            "\n",
            "eng.train           100%[===================>]   3.13M  --.-KB/s    in 0.09s   \n",
            "\n",
            "2023-10-27 13:23:46 (35.7 MB/s) - ‘eng.train’ saved [3283420/3283420]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0296Om2C5anY"
      },
      "source": [
        "# 3. Train Deep Learning Classifier using nlu.load('train.ner')\n",
        "\n",
        "You dataset label column should be named 'y' and the feature column with text data should be named 'text'"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "3ZIPkRkWftBG",
        "outputId": "fc7f1e4b-6286-4171-8b65-46d7567e607b"
      },
      "source": [
        "from johnsnowlabs import nlp\n",
        "# load a trainable pipeline by specifying the train. prefix  and fit it on a dataset with label and text columns\n",
        "# Since there are no\n",
        "train_path = '/content/eng.train'\n",
        "trainable_pipe = nlp.load('train.ner')\n",
        "fitted_pipe = trainable_pipe.fit(dataset_path=train_path)\n",
        "\n",
        "# predict with the trainable pipeline on dataset and get predictions\n",
        "preds = fitted_pipe.predict('Donald Trump and Angela Merkel dont share many oppinions')\n",
        "preds"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Warning::Spark Session already created, some configs may not take.\n",
            "small_bert_L2_128 download started this may take some time.\n",
            "Approximate size to download 16.1 MB\n",
            "[OK!]\n",
            "sentence_detector_dl download started this may take some time.\n",
            "Approximate size to download 354.6 KB\n",
            "[OK!]\n",
            "Warning::Spark Session already created, some configs may not take.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "                                            document        entities_ner  \\\n",
              "0  Donald Trump and Angela Merkel dont share many...        Donald Trump   \n",
              "0  Donald Trump and Angela Merkel dont share many...  Angela Merkel dont   \n",
              "\n",
              "  entities_ner_class entities_ner_confidence entities_ner_origin_chunk  \\\n",
              "0                PER                  0.9544                         0   \n",
              "0                PER              0.88476664                         1   \n",
              "\n",
              "  entities_ner_origin_sentence  \\\n",
              "0                            0   \n",
              "0                            0   \n",
              "\n",
              "                                 word_embedding_bert  \n",
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          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "owFhjKqzQiv5",
        "outputId": "7be23961-d30c-4176-cb25-ad9b6920806c"
      },
      "source": [
        "# Check out the Parameters of the NER model we can configure\n",
        "trainable_pipe.print_info()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",
            ">>> component_list['document_assembler'] has settable params:\n",
            "component_list['document_assembler'].setCleanupMode('shrink')                    | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : shrink\n",
            ">>> component_list['bert_embeddings@small_bert_L2_128'] has settable params:\n",
            "component_list['bert_embeddings@small_bert_L2_128'].setBatchSize(8)              | Info: Size of every batch | Currently set to : 8\n",
            "component_list['bert_embeddings@small_bert_L2_128'].setEngine('tensorflow')      | Info: Deep Learning engine used for this model | Currently set to : tensorflow\n",
            "component_list['bert_embeddings@small_bert_L2_128'].setMaxSentenceLength(128)    | Info: Max sentence length to process | Currently set to : 128\n",
            "component_list['bert_embeddings@small_bert_L2_128'].setDimension(128)            | Info: Number of embedding dimensions | Currently set to : 128\n",
            "component_list['bert_embeddings@small_bert_L2_128'].setCaseSensitive(False)      | Info: whether to ignore case in tokens for embeddings matching | Currently set to : False\n",
            "component_list['bert_embeddings@small_bert_L2_128'].setStorageRef('small_bert_L2_128')  | Info: unique reference name for identification | Currently set to : small_bert_L2_128\n",
            ">>> component_list['tokenizer'] has settable params:\n",
            "component_list['tokenizer'].setTargetPattern('\\S+')                              | Info: pattern to grab from text as token candidates. Defaults \\S+ | Currently set to : \\S+\n",
            "component_list['tokenizer'].setContextChars(['.', ',', ';', ':', '!', '?', '*', '-', '(', ')', '\"', \"'\"])  | Info: character list used to separate from token boundaries | Currently set to : ['.', ',', ';', ':', '!', '?', '*', '-', '(', ')', '\"', \"'\"]\n",
            "component_list['tokenizer'].setCaseSensitiveExceptions(True)                     | Info: Whether to care for case sensitiveness in exceptions | Currently set to : True\n",
            "component_list['tokenizer'].setMinLength(0)                                      | Info: Set the minimum allowed length for each token | Currently set to : 0\n",
            "component_list['tokenizer'].setMaxLength(99999)                                  | Info: Set the maximum allowed length for each token | Currently set to : 99999\n",
            ">>> component_list['chunk_converter@entities'] has settable params:\n",
            ">>> component_list['ner_dl@small_bert_L2_128'] has settable params:\n",
            "component_list['ner_dl@small_bert_L2_128'].setBatchSize(8)                       | Info: Size of every batch | Currently set to : 8\n",
            "component_list['ner_dl@small_bert_L2_128'].setEngine('tensorflow')               | Info: Deep Learning engine used for this model | Currently set to : tensorflow\n",
            "component_list['ner_dl@small_bert_L2_128'].setIncludeConfidence(True)            | Info: whether to include confidence scores in annotation metadata | Currently set to : True\n",
            "component_list['ner_dl@small_bert_L2_128'].setIncludeAllConfidenceScores(False)  | Info: whether to include all confidence scores in annotation metadata or just the score of the predicted tag | Currently set to : False\n",
            "component_list['ner_dl@small_bert_L2_128'].setStorageRef('small_bert_L2_128')    | Info: unique reference name for identification | Currently set to : small_bert_L2_128\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "25RTuUXMFyEA"
      },
      "source": [
        "# 4. Lets use BERT embeddings instead of the default Glove_100d ones!"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nlp.nlu.print_components(action='embed')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ixhWEXz6Yc7z",
        "outputId": "b03dbb2d-7e23-43fc-a642-2a1f941f2719"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For language <af> NLU provides the following Models : \n",
            "nlu.load('af.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <als> NLU provides the following Models : \n",
            "nlu.load('als.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <am> NLU provides the following Models : \n",
            "nlu.load('am.embed.am_roberta') returns Spark NLP model_anno_obj roberta_embeddings_am_roberta\n",
            "nlu.load('am.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "nlu.load('am.embed.xlm_roberta') returns Spark NLP model_anno_obj xlm_roberta_base_finetuned_amharic\n",
            "For language <an> NLU provides the following Models : \n",
            "nlu.load('an.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <ar> NLU provides the following Models : \n",
            "nlu.load('ar.embed') returns Spark NLP model_anno_obj arabic_w2v_cc_300d\n",
            "nlu.load('ar.embed.AraBertMo_base_V1') returns Spark NLP model_anno_obj bert_embeddings_AraBertMo_base_V1\n",
            "nlu.load('ar.embed.Ara_DialectBERT') returns Spark NLP model_anno_obj bert_embeddings_Ara_DialectBERT\n",
            "nlu.load('ar.embed.DarijaBERT') returns Spark NLP model_anno_obj bert_embeddings_DarijaBERT\n",
            "nlu.load('ar.embed.MARBERT') returns Spark NLP model_anno_obj bert_embeddings_MARBERT\n",
            "nlu.load('ar.embed.MARBERTv2') returns Spark NLP model_anno_obj bert_embeddings_MARBERTv2\n",
            "nlu.load('ar.embed.albert') returns Spark NLP model_anno_obj albert_embeddings_albert_base_arabic\n",
            "nlu.load('ar.embed.albert_large_arabic') returns Spark NLP model_anno_obj albert_embeddings_albert_large_arabic\n",
            "nlu.load('ar.embed.albert_xlarge_arabic') returns Spark NLP model_anno_obj albert_embeddings_albert_xlarge_arabic\n",
            "nlu.load('ar.embed.aner') returns Spark NLP model_anno_obj arabic_w2v_cc_300d\n",
            "nlu.load('ar.embed.aner.300d') returns Spark NLP model_anno_obj arabic_w2v_cc_300d\n",
            "nlu.load('ar.embed.arabert_c19') returns Spark NLP model_anno_obj bert_embeddings_arabert_c19\n",
            "nlu.load('ar.embed.arbert') returns Spark NLP model_anno_obj bert_embeddings_ARBERT\n",
            "nlu.load('ar.embed.bert') returns Spark NLP model_anno_obj bert_embeddings_arbert\n",
            "nlu.load('ar.embed.bert.base') returns Spark NLP model_anno_obj bert_embeddings_base_arabert\n",
            "nlu.load('ar.embed.bert.base.by_asafaya') returns Spark NLP model_anno_obj bert_embeddings_base_arabic\n",
            "nlu.load('ar.embed.bert.base.v1.by_aubmindlab') returns Spark NLP model_anno_obj bert_embeddings_base_arabertv01\n",
            "nlu.load('ar.embed.bert.base.v2.by_aubmindlab') returns Spark NLP model_anno_obj bert_embeddings_base_arabertv02\n",
            "nlu.load('ar.embed.bert.base_mix.by_camel_lab') returns Spark NLP model_anno_obj bert_embeddings_base_arabic_camel_mix\n",
            "nlu.load('ar.embed.bert.base_msa.by_camel_lab') returns Spark NLP model_anno_obj bert_embeddings_base_arabic_camel_msa\n",
            "nlu.load('ar.embed.bert.base_msa_eighth.by_camel_lab') returns Spark NLP model_anno_obj bert_embeddings_base_arabic_camel_msa_eighth\n",
            "nlu.load('ar.embed.bert.base_msa_half.by_camel_lab') returns Spark NLP model_anno_obj bert_embeddings_base_arabic_camel_msa_half\n",
            "nlu.load('ar.embed.bert.base_msa_quarter.by_camel_lab') returns Spark NLP model_anno_obj bert_embeddings_base_arabic_camel_msa_quarter\n",
            "nlu.load('ar.embed.bert.base_msa_sixteenth.by_camel_lab') returns Spark NLP model_anno_obj bert_embeddings_base_arabic_camel_msa_sixteenth\n",
            "nlu.load('ar.embed.bert.by_ubc_nlp') returns Spark NLP model_anno_obj bert_embeddings_marbert\n",
            "nlu.load('ar.embed.bert.cased_base') returns Spark NLP model_anno_obj bert_embeddings_base_ar_cased\n",
            "nlu.load('ar.embed.bert.large') returns Spark NLP model_anno_obj bert_embeddings_large_arabertv02\n",
            "nlu.load('ar.embed.bert.large.by_asafaya') returns Spark NLP model_anno_obj bert_embeddings_large_arabic\n",
            "nlu.load('ar.embed.bert.medium') returns Spark NLP model_anno_obj bert_embeddings_medium_arabic\n",
            "nlu.load('ar.embed.bert.mini') returns Spark NLP model_anno_obj bert_embeddings_mini_arabic\n",
            "nlu.load('ar.embed.bert.v2') returns Spark NLP model_anno_obj bert_embeddings_marbertv2\n",
            "nlu.load('ar.embed.bert.v2_base') returns Spark NLP model_anno_obj bert_embeddings_base_arabertv2\n",
            "nlu.load('ar.embed.bert.v2_large') returns Spark NLP model_anno_obj bert_embeddings_large_arabertv2\n",
            "nlu.load('ar.embed.bert_base_arabert') returns Spark NLP model_anno_obj bert_embeddings_bert_base_arabert\n",
            "nlu.load('ar.embed.bert_base_arabertv01') returns Spark NLP model_anno_obj bert_embeddings_bert_base_arabertv01\n",
            "nlu.load('ar.embed.bert_base_arabertv02') returns Spark NLP model_anno_obj bert_embeddings_bert_base_arabertv02\n",
            "nlu.load('ar.embed.bert_base_arabertv02_twitter') returns Spark NLP model_anno_obj bert_embeddings_bert_base_arabertv02_twitter\n",
            "nlu.load('ar.embed.bert_base_arabertv2') returns Spark NLP model_anno_obj bert_embeddings_bert_base_arabertv2\n",
            "nlu.load('ar.embed.bert_base_arabic') returns Spark NLP model_anno_obj bert_embeddings_bert_base_arabic\n",
            "nlu.load('ar.embed.bert_base_arabic_camelbert_mix') returns Spark NLP model_anno_obj bert_embeddings_bert_base_arabic_camelbert_mix\n",
            "nlu.load('ar.embed.bert_base_arabic_camelbert_msa') returns Spark NLP model_anno_obj bert_embeddings_bert_base_arabic_camelbert_msa\n",
            "nlu.load('ar.embed.bert_base_arabic_camelbert_msa_eighth') returns Spark NLP model_anno_obj bert_embeddings_bert_base_arabic_camelbert_msa_eighth\n",
            "nlu.load('ar.embed.bert_base_arabic_camelbert_msa_half') returns Spark NLP model_anno_obj bert_embeddings_bert_base_arabic_camelbert_msa_half\n",
            "nlu.load('ar.embed.bert_base_arabic_camelbert_msa_quarter') returns Spark NLP model_anno_obj bert_embeddings_bert_base_arabic_camelbert_msa_quarter\n",
            "nlu.load('ar.embed.bert_base_arabic_camelbert_msa_sixteenth') returns Spark NLP model_anno_obj bert_embeddings_bert_base_arabic_camelbert_msa_sixteenth\n",
            "nlu.load('ar.embed.bert_base_qarib') returns Spark NLP model_anno_obj bert_embeddings_bert_base_qarib\n",
            "nlu.load('ar.embed.bert_base_qarib60_1790k') returns Spark NLP model_anno_obj bert_embeddings_bert_base_qarib60_1790k\n",
            "nlu.load('ar.embed.bert_base_qarib60_860k') returns Spark NLP model_anno_obj bert_embeddings_bert_base_qarib60_860k\n",
            "nlu.load('ar.embed.bert_large_arabertv02') returns Spark NLP model_anno_obj bert_embeddings_bert_large_arabertv02\n",
            "nlu.load('ar.embed.bert_large_arabertv02_twitter') returns Spark NLP model_anno_obj bert_embeddings_bert_large_arabertv02_twitter\n",
            "nlu.load('ar.embed.bert_large_arabertv2') returns Spark NLP model_anno_obj bert_embeddings_bert_large_arabertv2\n",
            "nlu.load('ar.embed.bert_large_arabic') returns Spark NLP model_anno_obj bert_embeddings_bert_large_arabic\n",
            "nlu.load('ar.embed.bert_medium_arabic') returns Spark NLP model_anno_obj bert_embeddings_bert_medium_arabic\n",
            "nlu.load('ar.embed.bert_mini_arabic') returns Spark NLP model_anno_obj bert_embeddings_bert_mini_arabic\n",
            "nlu.load('ar.embed.cbow') returns Spark NLP model_anno_obj arabic_w2v_cc_300d\n",
            "nlu.load('ar.embed.cbow.300d') returns Spark NLP model_anno_obj arabic_w2v_cc_300d\n",
            "nlu.load('ar.embed.distilbert') returns Spark NLP model_anno_obj distilbert_embeddings_distilbert_base_ar_cased\n",
            "nlu.load('ar.embed.dziribert') returns Spark NLP model_anno_obj bert_embeddings_dziribert\n",
            "nlu.load('ar.embed.electra.base') returns Spark NLP model_anno_obj electra_embeddings_araelectra_base_generator\n",
            "nlu.load('ar.embed.glove') returns Spark NLP model_anno_obj arabic_w2v_cc_300d\n",
            "nlu.load('ar.embed.mbert_ar_c19') returns Spark NLP model_anno_obj bert_embeddings_mbert_ar_c19\n",
            "nlu.load('ar.embed.multi_dialect_bert_base_arabic') returns Spark NLP model_anno_obj bert_embeddings_multi_dialect_bert_base_arabic\n",
            "For language <arz> NLU provides the following Models : \n",
            "nlu.load('arz.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <as> NLU provides the following Models : \n",
            "nlu.load('as.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <ast> NLU provides the following Models : \n",
            "nlu.load('ast.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <az> NLU provides the following Models : \n",
            "nlu.load('az.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <azb> NLU provides the following Models : \n",
            "nlu.load('azb.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <ba> NLU provides the following Models : \n",
            "nlu.load('ba.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <bar> NLU provides the following Models : \n",
            "nlu.load('bar.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <bcl> NLU provides the following Models : \n",
            "nlu.load('bcl.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <be> NLU provides the following Models : \n",
            "nlu.load('be.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <bg> NLU provides the following Models : \n",
            "nlu.load('bg.embed.bert.cased_base') returns Spark NLP model_anno_obj bert_embeddings_base_bg_cased\n",
            "nlu.load('bg.embed.roberta.base') returns Spark NLP model_anno_obj roberta_embeddings_base_bulgarian\n",
            "nlu.load('bg.embed.roberta.small') returns Spark NLP model_anno_obj roberta_embeddings_small_bulgarian\n",
            "nlu.load('bg.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <bh> NLU provides the following Models : \n",
            "nlu.load('bh.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <bn> NLU provides the following Models : \n",
            "nlu.load('bn.embed') returns Spark NLP model_anno_obj bengali_cc_300d\n",
            "nlu.load('bn.embed.bangala_bert') returns Spark NLP model_anno_obj bert_embeddings_bangla_bert_base\n",
            "nlu.load('bn.embed.bangla_bert') returns Spark NLP model_anno_obj bert_embeddings_bangla_bert\n",
            "nlu.load('bn.embed.bert') returns Spark NLP model_anno_obj bert_embeddings_indic_transformers\n",
            "nlu.load('bn.embed.bert.base') returns Spark NLP model_anno_obj bert_embeddings_bangla_base\n",
            "nlu.load('bn.embed.distil_bert') returns Spark NLP model_anno_obj distilbert_embeddings_indic_transformers\n",
            "nlu.load('bn.embed.glove') returns Spark NLP model_anno_obj bengali_cc_300d\n",
            "nlu.load('bn.embed.indic_transformers_bn_bert') returns Spark NLP model_anno_obj bert_embeddings_indic_transformers_bn_bert\n",
            "nlu.load('bn.embed.indic_transformers_bn_distilbert') returns Spark NLP model_anno_obj distilbert_embeddings_indic_transformers_bn_distilbert\n",
            "nlu.load('bn.embed.muril_adapted_local') returns Spark NLP model_anno_obj bert_embeddings_muril_adapted_local\n",
            "nlu.load('bn.embed.roberta') returns Spark NLP model_anno_obj roberta_embeddings_indic_transformers\n",
            "nlu.load('bn.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "nlu.load('bn.embed.xlmr_roberta') returns Spark NLP model_anno_obj xlmroberta_embeddings_indic_transformers_bn_xlmroberta\n",
            "For language <bo> NLU provides the following Models : \n",
            "nlu.load('bo.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <bpy> NLU provides the following Models : \n",
            "nlu.load('bpy.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <br> NLU provides the following Models : \n",
            "nlu.load('br.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <bs> NLU provides the following Models : \n",
            "nlu.load('bs.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <ca> NLU provides the following Models : \n",
            "nlu.load('ca.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <ce> NLU provides the following Models : \n",
            "nlu.load('ce.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <ceb> NLU provides the following Models : \n",
            "nlu.load('ceb.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <co> NLU provides the following Models : \n",
            "nlu.load('co.embed.roberta.small') returns Spark NLP model_anno_obj roberta_embeddings_codeberta_small_v1\n",
            "nlu.load('co.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <cs> NLU provides the following Models : \n",
            "nlu.load('cs.embed.bert') returns Spark NLP model_anno_obj bert_embeddings_fernet_c5\n",
            "nlu.load('cs.embed.roberta.news.') returns Spark NLP model_anno_obj roberta_embeddings_fernet_news\n",
            "nlu.load('cs.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <cv> NLU provides the following Models : \n",
            "nlu.load('cv.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <cy> NLU provides the following Models : \n",
            "nlu.load('cy.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <da> NLU provides the following Models : \n",
            "nlu.load('da.embed.bert.cased_base') returns Spark NLP model_anno_obj bert_embeddings_base_da_cased\n",
            "nlu.load('da.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <de> NLU provides the following Models : \n",
            "nlu.load('de.embed.albert_german_ner') returns Spark NLP model_anno_obj albert_embeddings_albert_german_ner\n",
            "nlu.load('de.embed.bert') returns Spark NLP model_anno_obj bert_base_german_cased\n",
            "nlu.load('de.embed.bert.base') returns Spark NLP model_anno_obj bert_embeddings_g_base\n",
            "nlu.load('de.embed.bert.by_smanjil') returns Spark NLP model_anno_obj bert_embeddings_german_medbert\n",
            "nlu.load('de.embed.bert.cased_base') returns Spark NLP model_anno_obj bert_embeddings_base_de_cased\n",
            "nlu.load('de.embed.bert.cased_base.by_dbmdz') returns Spark NLP model_anno_obj bert_embeddings_dbmdz_base_german_cased\n",
            "nlu.load('de.embed.bert.cased_base.by_uploaded by huggingface') returns Spark NLP model_anno_obj bert_embeddings_base_german_cased\n",
            "nlu.load('de.embed.bert.finance') returns Spark NLP model_anno_obj bert_sentence_embeddings_financial\n",
            "nlu.load('de.embed.bert.large') returns Spark NLP model_anno_obj bert_embeddings_g_large\n",
            "nlu.load('de.embed.bert.uncased') returns Spark NLP model_anno_obj bert_base_german_uncased\n",
            "nlu.load('de.embed.bert.uncased_base') returns Spark NLP model_anno_obj bert_embeddings_base_german_uncased\n",
            "nlu.load('de.embed.bert_base_5lang_cased') returns Spark NLP model_anno_obj bert_embeddings_bert_base_5lang_cased\n",
            "nlu.load('de.embed.bert_base_de_cased') returns Spark NLP model_anno_obj bert_embeddings_bert_base_de_cased\n",
            "nlu.load('de.embed.bert_base_german_cased_oldvocab') returns Spark NLP model_anno_obj bert_embeddings_bert_base_german_cased_oldvocab\n",
            "nlu.load('de.embed.bert_base_german_dbmdz_cased') returns Spark NLP model_anno_obj bert_embeddings_bert_base_german_dbmdz_cased\n",
            "nlu.load('de.embed.bert_base_german_dbmdz_uncased') returns Spark NLP model_anno_obj bert_embeddings_bert_base_german_dbmdz_uncased\n",
            "nlu.load('de.embed.bert_base_german_uncased') returns Spark NLP model_anno_obj bert_embeddings_bert_base_german_uncased\n",
            "nlu.load('de.embed.bert_base_historical_german_rw_cased') returns Spark NLP model_anno_obj bert_embeddings_bert_base_historical_german_rw_cased\n",
            "nlu.load('de.embed.distilbert_base_de_cased') returns Spark NLP model_anno_obj distilbert_embeddings_distilbert_base_de_cased\n",
            "nlu.load('de.embed.distilbert_base_german_cased') returns Spark NLP model_anno_obj distilbert_embeddings_distilbert_base_german_cased\n",
            "nlu.load('de.embed.electra.base') returns Spark NLP model_anno_obj electra_embeddings_gelectra_base_generator\n",
            "nlu.load('de.embed.electra.cased_base_64d') returns Spark NLP model_anno_obj electra_embeddings_electra_base_gc4_64k_0_cased_generator\n",
            "nlu.load('de.embed.electra.cased_base_gc4_64k_100000.by_stefan_it') returns Spark NLP model_anno_obj electra_embeddings_electra_base_gc4_64k_100000_cased_generator\n",
            "nlu.load('de.embed.electra.cased_base_gc4_64k_1000000.by_stefan_it') returns Spark NLP model_anno_obj electra_embeddings_electra_base_gc4_64k_1000000_cased_generator\n",
            "nlu.load('de.embed.electra.cased_base_gc4_64k_200000.by_stefan_it') returns Spark NLP model_anno_obj electra_embeddings_electra_base_gc4_64k_200000_cased_generator\n",
            "nlu.load('de.embed.electra.cased_base_gc4_64k_300000.by_stefan_it') returns Spark NLP model_anno_obj electra_embeddings_electra_base_gc4_64k_300000_cased_generator\n",
            "nlu.load('de.embed.electra.cased_base_gc4_64k_400000.by_stefan_it') returns Spark NLP model_anno_obj electra_embeddings_electra_base_gc4_64k_400000_cased_generator\n",
            "nlu.load('de.embed.electra.cased_base_gc4_64k_500000.by_stefan_it') returns Spark NLP model_anno_obj electra_embeddings_electra_base_gc4_64k_500000_cased_generator\n",
            "nlu.load('de.embed.electra.cased_base_gc4_64k_600000.by_stefan_it') returns Spark NLP model_anno_obj electra_embeddings_electra_base_gc4_64k_600000_cased_generator\n",
            "nlu.load('de.embed.electra.cased_base_gc4_64k_700000.by_stefan_it') returns Spark NLP model_anno_obj electra_embeddings_electra_base_gc4_64k_700000_cased_generator\n",
            "nlu.load('de.embed.electra.cased_base_gc4_64k_800000.by_stefan_it') returns Spark NLP model_anno_obj electra_embeddings_electra_base_gc4_64k_800000_cased_generator\n",
            "nlu.load('de.embed.electra.cased_base_gc4_64k_900000.by_stefan_it') returns Spark NLP model_anno_obj electra_embeddings_electra_base_gc4_64k_900000_cased_generator\n",
            "nlu.load('de.embed.electra.large') returns Spark NLP model_anno_obj electra_embeddings_gelectra_large_generator\n",
            "nlu.load('de.embed.gbert_base') returns Spark NLP model_anno_obj bert_embeddings_gbert_base\n",
            "nlu.load('de.embed.gbert_large') returns Spark NLP model_anno_obj bert_embeddings_gbert_large\n",
            "nlu.load('de.embed.german_financial_statements_bert') returns Spark NLP model_anno_obj bert_embeddings_german_financial_statements_bert\n",
            "nlu.load('de.embed.medbert') returns Spark NLP model_anno_obj bert_embeddings_German_MedBERT\n",
            "nlu.load('de.embed.roberta') returns Spark NLP model_anno_obj roberta_embeddings_hotelbert\n",
            "nlu.load('de.embed.roberta.small') returns Spark NLP model_anno_obj roberta_embeddings_hotelbert_small\n",
            "nlu.load('de.embed.roberta_base_wechsel_german') returns Spark NLP model_anno_obj roberta_embeddings_roberta_base_wechsel_german\n",
            "For language <diq> NLU provides the following Models : \n",
            "nlu.load('diq.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <dv> NLU provides the following Models : \n",
            "nlu.load('dv.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <el> NLU provides the following Models : \n",
            "nlu.load('el.embed.bert.base_uncased') returns Spark NLP model_anno_obj bert_base_uncased\n",
            "nlu.load('el.embed.bert.cased_base') returns Spark NLP model_anno_obj bert_embeddings_base_el_cased\n",
            "nlu.load('el.embed.bert.uncased_base') returns Spark NLP model_anno_obj bert_embeddings_greeksocial_base_greek_uncased_v1\n",
            "nlu.load('el.embed.roberta.uncased_base') returns Spark NLP model_anno_obj roberta_embeddings_palobert_base_greek_uncased_v1\n",
            "For language <eml> NLU provides the following Models : \n",
            "nlu.load('eml.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <en> NLU provides the following Models : \n",
            "nlu.load('en.embed') returns Spark NLP model_anno_obj glove_100d\n",
            "nlu.load('en.embed.Bible_roberta_base') returns Spark NLP model_anno_obj roberta_embeddings_Bible_roberta_base\n",
            "nlu.load('en.embed.COVID_SciBERT') returns Spark NLP model_anno_obj bert_embeddings_COVID_SciBERT\n",
            "nlu.load('en.embed.DiLBERT') returns Spark NLP model_anno_obj bert_embeddings_DiLBERT\n",
            "nlu.load('en.embed.FinancialBERT') returns Spark NLP model_anno_obj bert_embeddings_FinancialBERT\n",
            "nlu.load('en.embed.SecBERT') returns Spark NLP model_anno_obj bert_embeddings_SecBERT\n",
            "nlu.load('en.embed.SecRoBERTa') returns Spark NLP model_anno_obj roberta_embeddings_SecRoBERTa\n",
            "nlu.load('en.embed.agriculture_bert_uncased') returns Spark NLP model_anno_obj bert_embeddings_agriculture_bert_uncased\n",
            "nlu.load('en.embed.albert') returns Spark NLP model_anno_obj albert_base_uncased\n",
            "nlu.load('en.embed.albert.base_uncased') returns Spark NLP model_anno_obj albert_base_uncased\n",
            "nlu.load('en.embed.albert.large_uncased') returns Spark NLP model_anno_obj albert_large_uncased\n",
            "nlu.load('en.embed.albert.xlarge_uncased') returns Spark NLP model_anno_obj albert_xlarge_uncased\n",
            "nlu.load('en.embed.albert.xxlarge_uncased') returns Spark NLP model_anno_obj albert_xxlarge_uncased\n",
            "nlu.load('en.embed.albert_base_v1') returns Spark NLP model_anno_obj albert_embeddings_albert_base_v1\n",
            "nlu.load('en.embed.albert_xlarge_v1') returns Spark NLP model_anno_obj albert_embeddings_albert_xlarge_v1\n",
            "nlu.load('en.embed.albert_xxlarge_v1') returns Spark NLP model_anno_obj albert_embeddings_albert_xxlarge_v1\n",
            "nlu.load('en.embed.bert') returns Spark NLP model_anno_obj bert_base_uncased\n",
            "nlu.load('en.embed.bert.base') returns Spark NLP model_anno_obj bert_embeddings_v_2021_base\n",
            "nlu.load('en.embed.bert.base_cased') returns Spark NLP model_anno_obj bert_base_cased\n",
            "nlu.load('en.embed.bert.base_uncased') returns Spark NLP model_anno_obj bert_base_uncased\n",
            "nlu.load('en.embed.bert.base_uncased_legal') returns Spark NLP model_anno_obj bert_base_uncased_legal\n",
            "nlu.load('en.embed.bert.by_anferico') returns Spark NLP model_anno_obj bert_embeddings_for_patents\n",
            "nlu.load('en.embed.bert.by_beatrice_portelli') returns Spark NLP model_anno_obj bert_embeddings_dilbert\n",
            "nlu.load('en.embed.bert.by_law_ai') returns Spark NLP model_anno_obj bert_embeddings_incaselawbert\n",
            "nlu.load('en.embed.bert.by_philschmid') returns Spark NLP model_anno_obj bert_embeddings_fin_pretrain_yiyanghkust\n",
            "nlu.load('en.embed.bert.cased_base') returns Spark NLP model_anno_obj bert_embeddings_jobbert_base_cased\n",
            "nlu.load('en.embed.bert.cased_base.by_ayansinha') returns Spark NLP model_anno_obj bert_embeddings_lic_class_scancode_base_cased_l32_1\n",
            "nlu.load('en.embed.bert.cased_base.by_geotrend') returns Spark NLP model_anno_obj bert_embeddings_base_en_cased\n",
            "nlu.load('en.embed.bert.cased_base.by_model_attribution_challenge') returns Spark NLP model_anno_obj bert_embeddings_model_attribution_challenge_base_cased\n",
            "nlu.load('en.embed.bert.cased_base.by_uploaded by huggingface') returns Spark NLP model_anno_obj bert_embeddings_base_cased\n",
            "nlu.load('en.embed.bert.cased_large') returns Spark NLP model_anno_obj bert_embeddings_large_cased\n",
            "nlu.load('en.embed.bert.cased_large_whole_word_masking') returns Spark NLP model_anno_obj bert_embeddings_large_cased_whole_word_masking\n",
            "nlu.load('en.embed.bert.contracts.large_small_finetuned_legal') returns Spark NLP model_anno_obj bert_embeddings_bert_small_finetuned_legal_contracts_larger20_5_1\n",
            "nlu.load('en.embed.bert.contracts.large_small_finetuned_legal.by_muhtasham') returns Spark NLP model_anno_obj bert_embeddings_bert_small_finetuned_legal_contracts_larger4010\n",
            "nlu.load('en.embed.bert.contracts.small_finetuned_legal') returns Spark NLP model_anno_obj bert_embeddings_bert_small_finetuned_legal_contracts10train10val\n",
            "nlu.load('en.embed.bert.contracts.uncased_base') returns Spark NLP model_anno_obj bert_base_uncased_contracts\n",
            "nlu.load('en.embed.bert.covid_bio_clinical.finetuned') returns Spark NLP model_anno_obj bert_embeddings_bioclinicalbert_finetuned_covid_papers\n",
            "nlu.load('en.embed.bert.large') returns Spark NLP model_anno_obj bert_embeddings_v_2021_large\n",
            "nlu.load('en.embed.bert.large_cased') returns Spark NLP model_anno_obj bert_large_cased\n",
            "nlu.load('en.embed.bert.large_legal_7m') returns Spark NLP model_anno_obj bert_embeddings_legalbert_large_1.7m_1\n",
            "nlu.load('en.embed.bert.large_legal_7m.by_pile_of_law') returns Spark NLP model_anno_obj bert_embeddings_legalbert_large_1.7m_2\n",
            "nlu.load('en.embed.bert.large_uncased') returns Spark NLP model_anno_obj bert_large_uncased\n",
            "nlu.load('en.embed.bert.legal') returns Spark NLP model_anno_obj bert_embeddings_inlegalbert\n",
            "nlu.load('en.embed.bert.phs') returns Spark NLP model_anno_obj bert_embeddings_phs_bert\n",
            "nlu.load('en.embed.bert.pubmed') returns Spark NLP model_anno_obj bert_pubmed\n",
            "nlu.load('en.embed.bert.pubmed.uncased') returns Spark NLP model_anno_obj bert_biomed_pubmed_uncased\n",
            "nlu.load('en.embed.bert.pubmed_squad2') returns Spark NLP model_anno_obj bert_pubmed_squad2\n",
            "nlu.load('en.embed.bert.small_L10_128') returns Spark NLP model_anno_obj small_bert_L10_128\n",
            "nlu.load('en.embed.bert.small_L10_256') returns Spark NLP model_anno_obj small_bert_L10_256\n",
            "nlu.load('en.embed.bert.small_L10_512') returns Spark NLP model_anno_obj small_bert_L10_512\n",
            "nlu.load('en.embed.bert.small_L10_768') returns Spark NLP model_anno_obj small_bert_L10_768\n",
            "nlu.load('en.embed.bert.small_L12_128') returns Spark NLP model_anno_obj small_bert_L12_128\n",
            "nlu.load('en.embed.bert.small_L12_256') returns Spark NLP model_anno_obj small_bert_L12_256\n",
            "nlu.load('en.embed.bert.small_L12_512') returns Spark NLP model_anno_obj small_bert_L12_512\n",
            "nlu.load('en.embed.bert.small_L12_768') returns Spark NLP model_anno_obj small_bert_L12_768\n",
            "nlu.load('en.embed.bert.small_L2_128') returns Spark NLP model_anno_obj small_bert_L2_128\n",
            "nlu.load('en.embed.bert.small_L2_256') returns Spark NLP model_anno_obj small_bert_L2_256\n",
            "nlu.load('en.embed.bert.small_L2_512') returns Spark NLP model_anno_obj small_bert_L2_512\n",
            "nlu.load('en.embed.bert.small_L2_768') returns Spark NLP model_anno_obj small_bert_L2_768\n",
            "nlu.load('en.embed.bert.small_L4_128') returns Spark NLP model_anno_obj small_bert_L4_128\n",
            "nlu.load('en.embed.bert.small_L4_256') returns Spark NLP model_anno_obj small_bert_L4_256\n",
            "nlu.load('en.embed.bert.small_L4_512') returns Spark NLP model_anno_obj small_bert_L4_512\n",
            "nlu.load('en.embed.bert.small_L4_768') returns Spark NLP model_anno_obj small_bert_L4_768\n",
            "nlu.load('en.embed.bert.small_L6_128') returns Spark NLP model_anno_obj small_bert_L6_128\n",
            "nlu.load('en.embed.bert.small_L6_256') returns Spark NLP model_anno_obj small_bert_L6_256\n",
            "nlu.load('en.embed.bert.small_L6_512') returns Spark NLP model_anno_obj small_bert_L6_512\n",
            "nlu.load('en.embed.bert.small_L6_768') returns Spark NLP model_anno_obj small_bert_L6_768\n",
            "nlu.load('en.embed.bert.small_L8_128') returns Spark NLP model_anno_obj small_bert_L8_128\n",
            "nlu.load('en.embed.bert.small_L8_256') returns Spark NLP model_anno_obj small_bert_L8_256\n",
            "nlu.load('en.embed.bert.small_L8_512') returns Spark NLP model_anno_obj small_bert_L8_512\n",
            "nlu.load('en.embed.bert.small_L8_768') returns Spark NLP model_anno_obj small_bert_L8_768\n",
            "nlu.load('en.embed.bert.small_finetuned_legal') returns Spark NLP model_anno_obj bert_embeddings_bert_small_finetuned_legal_definitions\n",
            "nlu.load('en.embed.bert.small_finetuned_legal.by_muhtasham') returns Spark NLP model_anno_obj bert_embeddings_bert_small_finetuned_legal_definitions_longer\n",
            "nlu.load('en.embed.bert.tiny_finetuned_legal') returns Spark NLP model_anno_obj bert_embeddings_bert_tiny_finetuned_legal_definitions\n",
            "nlu.load('en.embed.bert.uncased_base') returns Spark NLP model_anno_obj bert_embeddings_base_uncased\n",
            "nlu.load('en.embed.bert.uncased_base.by_model_attribution_challenge') returns Spark NLP model_anno_obj bert_embeddings_model_attribution_challenge_base_uncased\n",
            "nlu.load('en.embed.bert.uncased_base_finetuned_legal') returns Spark NLP model_anno_obj bert_embeddings_legal_bert_base_uncased_finetuned_rramicus\n",
            "nlu.load('en.embed.bert.uncased_base_finetuned_legal.by_hatemestinbejaia') returns Spark NLP model_anno_obj bert_embeddings_legal_bert_base_uncased_finetuned_ledgarscotus7\n",
            "nlu.load('en.embed.bert.uncased_large') returns Spark NLP model_anno_obj bert_embeddings_large_uncased\n",
            "nlu.load('en.embed.bert.uncased_large_whole_word_masking') returns Spark NLP model_anno_obj bert_embeddings_large_uncased_whole_word_masking\n",
            "nlu.load('en.embed.bert.wiki_books') returns Spark NLP model_anno_obj bert_wiki_books\n",
            "nlu.load('en.embed.bert.wiki_books_mnli') returns Spark NLP model_anno_obj bert_wiki_books_mnli\n",
            "nlu.load('en.embed.bert.wiki_books_qnli') returns Spark NLP model_anno_obj bert_wiki_books_qnli\n",
            "nlu.load('en.embed.bert.wiki_books_qqp') returns Spark NLP model_anno_obj bert_wiki_books_qqp\n",
            "nlu.load('en.embed.bert.wiki_books_squad2') returns Spark NLP model_anno_obj bert_wiki_books_squad2\n",
            "nlu.load('en.embed.bert.wiki_books_sst2') returns Spark NLP model_anno_obj bert_wiki_books_sst2\n",
            "nlu.load('en.embed.bert_base_5lang_cased') returns Spark NLP model_anno_obj bert_embeddings_bert_base_5lang_cased\n",
            "nlu.load('en.embed.bert_base_en_cased') returns Spark NLP model_anno_obj bert_embeddings_bert_base_en_cased\n",
            "nlu.load('en.embed.bert_base_uncased_dstc9') returns Spark NLP model_anno_obj bert_embeddings_bert_base_uncased_dstc9\n",
            "nlu.load('en.embed.bert_base_uncased_mnli_sparse_70_unstructured_no_classifier') returns Spark NLP model_anno_obj bert_embeddings_bert_base_uncased_mnli_sparse_70_unstructured_no_classifier\n",
            "nlu.load('en.embed.bert_base_uncased_sparse_70_unstructured') returns Spark NLP model_anno_obj bert_embeddings_bert_base_uncased_sparse_70_unstructured\n",
            "nlu.load('en.embed.bert_for_patents') returns Spark NLP model_anno_obj bert_embeddings_bert_for_patents\n",
            "nlu.load('en.embed.bert_large_cased_whole_word_masking') returns Spark NLP model_anno_obj bert_embeddings_bert_large_cased_whole_word_masking\n",
            "nlu.load('en.embed.bert_large_uncased_whole_word_masking') returns Spark NLP model_anno_obj bert_embeddings_bert_large_uncased_whole_word_masking\n",
            "nlu.load('en.embed.bert_political_election2020_twitter_mlm') returns Spark NLP model_anno_obj bert_embeddings_bert_political_election2020_twitter_mlm\n",
            "nlu.load('en.embed.biobert') returns Spark NLP model_anno_obj biobert_pubmed_base_cased\n",
            "nlu.load('en.embed.biobert.clinical_base_cased') returns Spark NLP model_anno_obj biobert_clinical_base_cased\n",
            "nlu.load('en.embed.biobert.discharge_base_cased') returns Spark NLP model_anno_obj biobert_discharge_base_cased\n",
            "nlu.load('en.embed.biobert.pmc_base_cased') returns Spark NLP model_anno_obj biobert_pmc_base_cased\n",
            "nlu.load('en.embed.biobert.pubmed.cased_base') returns Spark NLP model_anno_obj biobert_pubmed_base_cased_v1.2\n",
            "nlu.load('en.embed.biobert.pubmed_large_cased') returns Spark NLP model_anno_obj biobert_pubmed_large_cased\n",
            "nlu.load('en.embed.biobert.pubmed_pmc_base_cased') returns Spark NLP model_anno_obj biobert_pubmed_pmc_base_cased\n",
            "nlu.load('en.embed.bioformer.cased') returns Spark NLP model_anno_obj bert_embeddings_bioformer_cased_v1.0\n",
            "nlu.load('en.embed.chEMBL26_smiles_v2') returns Spark NLP model_anno_obj roberta_embeddings_chEMBL26_smiles_v2\n",
            "nlu.load('en.embed.chEMBL_smiles_v1') returns Spark NLP model_anno_obj roberta_embeddings_chEMBL_smiles_v1\n",
            "nlu.load('en.embed.chemical_bert_uncased') returns Spark NLP model_anno_obj bert_embeddings_chemical_bert_uncased\n",
            "nlu.load('en.embed.childes_bert') returns Spark NLP model_anno_obj bert_embeddings_childes_bert\n",
            "nlu.load('en.embed.clinical_pubmed_bert_base_128') returns Spark NLP model_anno_obj bert_embeddings_clinical_pubmed_bert_base_128\n",
            "nlu.load('en.embed.clinical_pubmed_bert_base_512') returns Spark NLP model_anno_obj bert_embeddings_clinical_pubmed_bert_base_512\n",
            "nlu.load('en.embed.covidbert') returns Spark NLP model_anno_obj covidbert_large_uncased\n",
            "nlu.load('en.embed.covidbert.large_uncased') returns Spark NLP model_anno_obj covidbert_large_uncased\n",
            "nlu.load('en.embed.crosloengual_bert') returns Spark NLP model_anno_obj bert_embeddings_crosloengual_bert\n",
            "nlu.load('en.embed.danbert_small_cased') returns Spark NLP model_anno_obj bert_embeddings_danbert_small_cased\n",
            "nlu.load('en.embed.deberta_base_uncased') returns Spark NLP model_anno_obj bert_embeddings_deberta_base_uncased\n",
            "nlu.load('en.embed.deberta_v3_base') returns Spark NLP model_anno_obj deberta_v3_base\n",
            "nlu.load('en.embed.deberta_v3_large') returns Spark NLP model_anno_obj deberta_v3_large\n",
            "nlu.load('en.embed.deberta_v3_small') returns Spark NLP model_anno_obj deberta_v3_small\n",
            "nlu.load('en.embed.deberta_v3_xsmall') returns Spark NLP model_anno_obj deberta_v3_xsmall\n",
            "nlu.load('en.embed.distil_bert') returns Spark NLP model_anno_obj distilbert_embeddings_test_text\n",
            "nlu.load('en.embed.distil_bert.finetuned') returns Spark NLP model_anno_obj distilbert_embeddings_finetuned_sarcasm_classification\n",
            "nlu.load('en.embed.distil_bert.uncased_base') returns Spark NLP model_anno_obj distilbert_embeddings_base_uncased\n",
            "nlu.load('en.embed.distil_bert.uncased_base_sparse_85_unstructured_pruneofa.by_intel') returns Spark NLP model_anno_obj distilbert_embeddings_base_uncased_sparse_85_unstructured_pruneofa\n",
            "nlu.load('en.embed.distil_bert.uncased_base_sparse_90_unstructured_pruneofa.by_intel') returns Spark NLP model_anno_obj distilbert_embeddings_base_uncased_sparse_90_unstructured_pruneofa\n",
            "nlu.load('en.embed.distilbert') returns Spark NLP model_anno_obj distilbert_base_cased\n",
            "nlu.load('en.embed.distilbert.base') returns Spark NLP model_anno_obj distilbert_base_cased\n",
            "nlu.load('en.embed.distilbert.base.uncased') returns Spark NLP model_anno_obj distilbert_base_uncased\n",
            "nlu.load('en.embed.distilbert_base_en_cased') returns Spark NLP model_anno_obj distilbert_embeddings_distilbert_base_en_cased\n",
            "nlu.load('en.embed.distilbert_base_uncased_sparse_85_unstructured_pruneofa') returns Spark NLP model_anno_obj distilbert_embeddings_distilbert_base_uncased_sparse_85_unstructured_pruneofa\n",
            "nlu.load('en.embed.distilbert_base_uncased_sparse_90_unstructured_pruneofa') returns Spark NLP model_anno_obj distilbert_embeddings_distilbert_base_uncased_sparse_90_unstructured_pruneofa\n",
            "nlu.load('en.embed.distilroberta') returns Spark NLP model_anno_obj distilroberta_base\n",
            "nlu.load('en.embed.distilroberta_base') returns Spark NLP model_anno_obj roberta_embeddings_distilroberta_base\n",
            "nlu.load('en.embed.distilroberta_base_climate_d') returns Spark NLP model_anno_obj roberta_embeddings_distilroberta_base_climate_d\n",
            "nlu.load('en.embed.distilroberta_base_climate_d_s') returns Spark NLP model_anno_obj roberta_embeddings_distilroberta_base_climate_d_s\n",
            "nlu.load('en.embed.distilroberta_base_climate_f') returns Spark NLP model_anno_obj roberta_embeddings_distilroberta_base_climate_f\n",
            "nlu.load('en.embed.distilroberta_base_finetuned_jira_qt_issue_title') returns Spark NLP model_anno_obj roberta_embeddings_distilroberta_base_finetuned_jira_qt_issue_title\n",
            "nlu.load('en.embed.distilroberta_base_finetuned_jira_qt_issue_titles_and_bodies') returns Spark NLP model_anno_obj roberta_embeddings_distilroberta_base_finetuned_jira_qt_issue_titles_and_bodies\n",
            "nlu.load('en.embed.e') returns Spark NLP model_anno_obj bert_biolink_base\n",
            "nlu.load('en.embed.electra') returns Spark NLP model_anno_obj electra_small_uncased\n",
            "nlu.load('en.embed.electra.base') returns Spark NLP model_anno_obj electra_embeddings_electra_base_generator\n",
            "nlu.load('en.embed.electra.base_uncased') returns Spark NLP model_anno_obj electra_base_uncased\n",
            "nlu.load('en.embed.electra.large') returns Spark NLP model_anno_obj electra_embeddings_electra_large_generator\n",
            "nlu.load('en.embed.electra.large_uncased') returns Spark NLP model_anno_obj electra_large_uncased\n",
            "nlu.load('en.embed.electra.medical') returns Spark NLP model_anno_obj electra_medal_acronym\n",
            "nlu.load('en.embed.electra.small') returns Spark NLP model_anno_obj electra_embeddings_electra_small_generator\n",
            "nlu.load('en.embed.electra.small_uncased') returns Spark NLP model_anno_obj electra_small_uncased\n",
            "nlu.load('en.embed.elmo') returns Spark NLP model_anno_obj elmo\n",
            "nlu.load('en.embed.fairlex_ecthr_minilm') returns Spark NLP model_anno_obj roberta_embeddings_fairlex_ecthr_minilm\n",
            "nlu.load('en.embed.fairlex_scotus_minilm') returns Spark NLP model_anno_obj roberta_embeddings_fairlex_scotus_minilm\n",
            "nlu.load('en.embed.false_positives_scancode_bert_base_uncased_L8_1') returns Spark NLP model_anno_obj bert_embeddings_false_positives_scancode_bert_base_uncased_L8_1\n",
            "nlu.load('en.embed.finbert_pretrain_yiyanghkust') returns Spark NLP model_anno_obj bert_embeddings_finbert_pretrain_yiyanghkust\n",
            "nlu.load('en.embed.finest_bert') returns Spark NLP model_anno_obj bert_embeddings_finest_bert\n",
            "nlu.load('en.embed.ge') returns Spark NLP model_anno_obj bert_biolink_large\n",
            "nlu.load('en.embed.glove') returns Spark NLP model_anno_obj glove_100d\n",
            "nlu.load('en.embed.glove.100d') returns Spark NLP model_anno_obj glove_100d\n",
            "nlu.load('en.embed.hateBERT') returns Spark NLP model_anno_obj bert_embeddings_hateBERT\n",
            "nlu.load('en.embed.legal.osf_lemmatized_legal') returns Spark NLP model_anno_obj word2vec_osf_lemmatized_legal\n",
            "nlu.load('en.embed.legal.osf_raw_legal') returns Spark NLP model_anno_obj word2vec_osf_raw_legal\n",
            "nlu.load('en.embed.legal.osf_replaced_lemmatized_legal') returns Spark NLP model_anno_obj word2vec_osf_replaced_lemmatized_legal\n",
            "nlu.load('en.embed.legal.osf_replaced_raw_legal') returns Spark NLP model_anno_obj word2vec_osf_replaced_raw_legal\n",
            "nlu.load('en.embed.legal_bert_base_uncased') returns Spark NLP model_anno_obj bert_embeddings_legal_bert_base_uncased\n",
            "nlu.load('en.embed.legal_bert_small_uncased') returns Spark NLP model_anno_obj bert_embeddings_legal_bert_small_uncased\n",
            "nlu.load('en.embed.legal_roberta_base') returns Spark NLP model_anno_obj roberta_embeddings_legal_roberta_base\n",
            "nlu.load('en.embed.legalbert.legal.by_zlucia') returns Spark NLP model_anno_obj bert_embeddings_legalbert\n",
            "nlu.load('en.embed.legalbert.legal.custom.by_zlucia') returns Spark NLP model_anno_obj bert_embeddings_custom_legalbert\n",
            "nlu.load('en.embed.lic_class_scancode_bert_base_cased_L32_1') returns Spark NLP model_anno_obj bert_embeddings_lic_class_scancode_bert_base_cased_L32_1\n",
            "nlu.load('en.embed.longformer') returns Spark NLP model_anno_obj longformer_base_4096\n",
            "nlu.load('en.embed.longformer.base_legal') returns Spark NLP model_anno_obj legal_longformer_base\n",
            "nlu.load('en.embed.longformer.clinical') returns Spark NLP model_anno_obj clinical_longformer\n",
            "nlu.load('en.embed.longformer.large') returns Spark NLP model_anno_obj longformer_large_4096\n",
            "nlu.load('en.embed.muppet_roberta_base') returns Spark NLP model_anno_obj roberta_embeddings_muppet_roberta_base\n",
            "nlu.load('en.embed.muppet_roberta_large') returns Spark NLP model_anno_obj roberta_embeddings_muppet_roberta_large\n",
            "nlu.load('en.embed.muril_adapted_local') returns Spark NLP model_anno_obj bert_embeddings_muril_adapted_local\n",
            "nlu.load('en.embed.netbert') returns Spark NLP model_anno_obj bert_embeddings_netbert\n",
            "nlu.load('en.embed.pmc_med_bio_mlm_roberta_large') returns Spark NLP model_anno_obj roberta_embeddings_pmc_med_bio_mlm_roberta_large\n",
            "nlu.load('en.embed.pos.uncased_base') returns Spark NLP model_anno_obj bert_embeddings_false_positives_scancode_base_uncased_l8_1\n",
            "nlu.load('en.embed.psych_search') returns Spark NLP model_anno_obj bert_embeddings_psych_search\n",
            "nlu.load('en.embed.roberta') returns Spark NLP model_anno_obj roberta_base\n",
            "nlu.load('en.embed.roberta.base') returns Spark NLP model_anno_obj roberta_base\n",
            "nlu.load('en.embed.roberta.base.by_model_attribution_challenge') returns Spark NLP model_anno_obj roberta_embeddings_model_attribution_challenge_base\n",
            "nlu.load('en.embed.roberta.base_finetuned') returns Spark NLP model_anno_obj roberta_embeddings_ruperta_base_finetuned_spa_constitution\n",
            "nlu.load('en.embed.roberta.base_legal') returns Spark NLP model_anno_obj roberta_embeddings_legal_base\n",
            "nlu.load('en.embed.roberta.cord19.1m') returns Spark NLP model_anno_obj roberta_embeddings_cord19_1m7k\n",
            "nlu.load('en.embed.roberta.distilled_base') returns Spark NLP model_anno_obj roberta_embeddings_distil_base\n",
            "nlu.load('en.embed.roberta.financial') returns Spark NLP model_anno_obj roberta_embeddings_financial\n",
            "nlu.load('en.embed.roberta.large') returns Spark NLP model_anno_obj roberta_large\n",
            "nlu.load('en.embed.roberta_pubmed') returns Spark NLP model_anno_obj roberta_embeddings_roberta_pubmed\n",
            "nlu.load('en.embed.scibert.cord19_scibert.finetuned') returns Spark NLP model_anno_obj bert_embeddings_scibert_scivocab_finetuned_cord19\n",
            "nlu.load('en.embed.scibert.covid_scibert.') returns Spark NLP model_anno_obj bert_embeddings_covid_scibert\n",
            "nlu.load('en.embed.sec_bert_base') returns Spark NLP model_anno_obj bert_embeddings_sec_bert_base\n",
            "nlu.load('en.embed.sec_bert_num') returns Spark NLP model_anno_obj bert_embeddings_sec_bert_num\n",
            "nlu.load('en.embed.sec_bert_sh') returns Spark NLP model_anno_obj bert_embeddings_sec_bert_sh\n",
            "nlu.load('en.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "nlu.load('en.embed.word2vec.gigaword') returns Spark NLP model_anno_obj word2vec_gigaword_300\n",
            "nlu.load('en.embed.word2vec.gigaword_wiki') returns Spark NLP model_anno_obj word2vec_gigaword_wiki_300\n",
            "nlu.load('en.embed.xlmr_roberta') returns Spark NLP model_anno_obj xlmroberta_embeddings_litlat_bert\n",
            "nlu.load('en.embed.xlnet') returns Spark NLP model_anno_obj xlnet_base_cased\n",
            "nlu.load('en.embed.xlnet_base_cased') returns Spark NLP model_anno_obj xlnet_base_cased\n",
            "nlu.load('en.embed.xlnet_large_cased') returns Spark NLP model_anno_obj xlnet_large_cased\n",
            "For language <eo> NLU provides the following Models : \n",
            "nlu.load('eo.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <es> NLU provides the following Models : \n",
            "nlu.load('es.embed.RoBERTalex') returns Spark NLP model_anno_obj roberta_embeddings_RoBERTalex\n",
            "nlu.load('es.embed.RuPERTa_base') returns Spark NLP model_anno_obj roberta_embeddings_RuPERTa_base\n",
            "nlu.load('es.embed.alberti_bert_base_multilingual_cased') returns Spark NLP model_anno_obj bert_embeddings_alberti_bert_base_multilingual_cased\n",
            "nlu.load('es.embed.bert.base_cased') returns Spark NLP model_anno_obj bert_base_cased\n",
            "nlu.load('es.embed.bert.base_legal') returns Spark NLP model_anno_obj legalectra_base\n",
            "nlu.load('es.embed.bert.base_uncased') returns Spark NLP model_anno_obj bert_base_uncased\n",
            "nlu.load('es.embed.bert.cased_base') returns Spark NLP model_anno_obj bert_embeddings_base_es_cased\n",
            "nlu.load('es.embed.bert.cased_base.by_dccuchile') returns Spark NLP model_anno_obj bert_embeddings_base_spanish_wwm_cased\n",
            "nlu.load('es.embed.bert.small_legal') returns Spark NLP model_anno_obj legalectra_small\n",
            "nlu.load('es.embed.bert.uncased_base') returns Spark NLP model_anno_obj bert_embeddings_base_spanish_wwm_uncased\n",
            "nlu.load('es.embed.bert_base_5lang_cased') returns Spark NLP model_anno_obj bert_embeddings_bert_base_5lang_cased\n",
            "nlu.load('es.embed.bert_base_es_cased') returns Spark NLP model_anno_obj bert_embeddings_bert_base_es_cased\n",
            "nlu.load('es.embed.bertin_base_gaussian') returns Spark NLP model_anno_obj roberta_embeddings_bertin_base_gaussian\n",
            "nlu.load('es.embed.bertin_base_gaussian_exp_512seqlen') returns Spark NLP model_anno_obj roberta_embeddings_bertin_base_gaussian_exp_512seqlen\n",
            "nlu.load('es.embed.bertin_base_random') returns Spark NLP model_anno_obj roberta_embeddings_bertin_base_random\n",
            "nlu.load('es.embed.bertin_base_random_exp_512seqlen') returns Spark NLP model_anno_obj roberta_embeddings_bertin_base_random_exp_512seqlen\n",
            "nlu.load('es.embed.bertin_base_stepwise') returns Spark NLP model_anno_obj roberta_embeddings_bertin_base_stepwise\n",
            "nlu.load('es.embed.bertin_base_stepwise_exp_512seqlen') returns Spark NLP model_anno_obj roberta_embeddings_bertin_base_stepwise_exp_512seqlen\n",
            "nlu.load('es.embed.bertin_roberta_base_spanish') returns Spark NLP model_anno_obj roberta_embeddings_bertin_roberta_base_spanish\n",
            "nlu.load('es.embed.bertin_roberta_large_spanish') returns Spark NLP model_anno_obj roberta_embeddings_bertin_roberta_large_spanish\n",
            "nlu.load('es.embed.beto_gn_base_cased') returns Spark NLP model_anno_obj bert_embeddings_beto_gn_base_cased\n",
            "nlu.load('es.embed.distilbert_base_es_cased') returns Spark NLP model_anno_obj distilbert_embeddings_distilbert_base_es_cased\n",
            "nlu.load('es.embed.distilbert_base_es_multilingual_cased') returns Spark NLP model_anno_obj distilbert_embeddings_distilbert_base_es_multilingual_cased\n",
            "nlu.load('es.embed.dpr_spanish_passage_encoder_allqa_base') returns Spark NLP model_anno_obj bert_embeddings_dpr_spanish_passage_encoder_allqa_base\n",
            "nlu.load('es.embed.dpr_spanish_passage_encoder_squades_base') returns Spark NLP model_anno_obj bert_embeddings_dpr_spanish_passage_encoder_squades_base\n",
            "nlu.load('es.embed.dpr_spanish_question_encoder_allqa_base') returns Spark NLP model_anno_obj bert_embeddings_dpr_spanish_question_encoder_allqa_base\n",
            "nlu.load('es.embed.dpr_spanish_question_encoder_squades_base') returns Spark NLP model_anno_obj bert_embeddings_dpr_spanish_question_encoder_squades_base\n",
            "nlu.load('es.embed.electra.base') returns Spark NLP model_anno_obj electra_embeddings_electricidad_base_generator\n",
            "nlu.load('es.embed.jurisbert') returns Spark NLP model_anno_obj roberta_embeddings_jurisbert\n",
            "nlu.load('es.embed.legal.cbow.cased_d100') returns Spark NLP model_anno_obj word2vec_cbow_legal_d100_cased\n",
            "nlu.load('es.embed.legal.cbow.cased_d300') returns Spark NLP model_anno_obj word2vec_cbow_legal_d300_cased\n",
            "nlu.load('es.embed.legal.cbow.cased_d50') returns Spark NLP model_anno_obj word2vec_cbow_legal_d50_cased\n",
            "nlu.load('es.embed.legal.cbow.uncased_d100') returns Spark NLP model_anno_obj word2vec_cbow_legal_d100_uncased\n",
            "nlu.load('es.embed.legal.cbow.uncased_d300') returns Spark NLP model_anno_obj word2vec_cbow_legal_d300_uncased\n",
            "nlu.load('es.embed.legal.cbow.uncased_d50') returns Spark NLP model_anno_obj word2vec_cbow_legal_d50_uncased\n",
            "nlu.load('es.embed.legal.skipgram.cased_d100') returns Spark NLP model_anno_obj word2vec_skipgram_legal_d100_cased\n",
            "nlu.load('es.embed.legal.skipgram.cased_d300') returns Spark NLP model_anno_obj word2vec_skipgram_legal_d300_cased\n",
            "nlu.load('es.embed.legal.skipgram.cased_d50') returns Spark NLP model_anno_obj word2vec_skipgram_legal_d50_cased\n",
            "nlu.load('es.embed.legal.skipgram.uncased_d100') returns Spark NLP model_anno_obj word2vec_skipgram_legal_d100_uncased\n",
            "nlu.load('es.embed.legal.skipgram.uncased_d300') returns Spark NLP model_anno_obj word2vec_skipgram_legal_d300_uncased\n",
            "nlu.load('es.embed.legal.skipgram.uncased_d50') returns Spark NLP model_anno_obj word2vec_skipgram_legal_d50_uncased\n",
            "nlu.load('es.embed.longformer.base_legal') returns Spark NLP model_anno_obj longformer_legal_base_8192\n",
            "nlu.load('es.embed.longformer.legal') returns Spark NLP model_anno_obj longformer_legal_embeddings\n",
            "nlu.load('es.embed.mlm_spanish_roberta_base') returns Spark NLP model_anno_obj roberta_embeddings_mlm_spanish_roberta_base\n",
            "nlu.load('es.embed.roberta_base_bne') returns Spark NLP model_anno_obj roberta_embeddings_roberta_base_bne\n",
            "nlu.load('es.embed.roberta_large_bne') returns Spark NLP model_anno_obj roberta_embeddings_roberta_large_bne\n",
            "nlu.load('es.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <et> NLU provides the following Models : \n",
            "nlu.load('et.embed.camembert') returns Spark NLP model_anno_obj camembert_embeddings_est_roberta\n",
            "nlu.load('et.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <eu> NLU provides the following Models : \n",
            "nlu.load('eu.embed.roberta') returns Spark NLP model_anno_obj roberta_embeddings_robasqu\n",
            "nlu.load('eu.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <fa> NLU provides the following Models : \n",
            "nlu.load('fa.embed') returns Spark NLP model_anno_obj persian_w2v_cc_300d\n",
            "nlu.load('fa.embed.albert') returns Spark NLP model_anno_obj albert_embeddings_albert_fa_base_v2\n",
            "nlu.load('fa.embed.albert_fa_zwnj_base_v2') returns Spark NLP model_anno_obj albert_embeddings_albert_fa_zwnj_base_v2\n",
            "nlu.load('fa.embed.bert.base') returns Spark NLP model_anno_obj bert_embeddings_fa_zwnj_base\n",
            "nlu.load('fa.embed.bert.uncased_base') returns Spark NLP model_anno_obj bert_embeddings_fa_base_uncased\n",
            "nlu.load('fa.embed.distilbert_fa_zwnj_base') returns Spark NLP model_anno_obj distilbert_embeddings_distilbert_fa_zwnj_base\n",
            "nlu.load('fa.embed.roberta_fa_zwnj_base') returns Spark NLP model_anno_obj roberta_embeddings_roberta_fa_zwnj_base\n",
            "nlu.load('fa.embed.word2vec') returns Spark NLP model_anno_obj persian_w2v_cc_300d\n",
            "nlu.load('fa.embed.word2vec.300d') returns Spark NLP model_anno_obj persian_w2v_cc_300d\n",
            "For language <fi> NLU provides the following Models : \n",
            "nlu.load('fi.embed.bert.cased_base') returns Spark NLP model_anno_obj bert_embeddings_base_finnish_cased_v1\n",
            "nlu.load('fi.embed.bert.uncased_base') returns Spark NLP model_anno_obj bert_embeddings_base_finnish_uncased_v1\n",
            "nlu.load('fi.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <fr> NLU provides the following Models : \n",
            "nlu.load('fr.embed.albert') returns Spark NLP model_anno_obj albert_embeddings_fralbert_base\n",
            "nlu.load('fr.embed.bert.cased_base') returns Spark NLP model_anno_obj bert_embeddings_base_fr_cased\n",
            "nlu.load('fr.embed.bert_5lang_cased') returns Spark NLP model_anno_obj bert_embeddings_bert_base_5lang_cased\n",
            "nlu.load('fr.embed.bert_base_fr_cased') returns Spark NLP model_anno_obj bert_embeddings_bert_base_fr_cased\n",
            "nlu.load('fr.embed.camembert') returns Spark NLP model_anno_obj camembert_embeddings_dummy\n",
            "nlu.load('fr.embed.camembert.91m_generic') returns Spark NLP model_anno_obj camembert_embeddings_generic_model_r91m\n",
            "nlu.load('fr.embed.camembert.adverse_drug_event_generic') returns Spark NLP model_anno_obj camembert_embeddings_adeimousa_generic_model\n",
            "nlu.load('fr.embed.camembert.base') returns Spark NLP model_anno_obj camembert_embeddings_dataikunlp_camembert_base\n",
            "nlu.load('fr.embed.camembert.by_ebtihal') returns Spark NLP model_anno_obj camembert_embeddings_arbertmo\n",
            "nlu.load('fr.embed.camembert.by_ghani_25') returns Spark NLP model_anno_obj camembert_embeddings_summfinfr\n",
            "nlu.load('fr.embed.camembert.by_hueynemud') returns Spark NLP model_anno_obj camembert_embeddings_das22_10_camembert_pretrained\n",
            "nlu.load('fr.embed.camembert.by_jodsa') returns Spark NLP model_anno_obj camembert_embeddings_camembert_mlm\n",
            "nlu.load('fr.embed.camembert.distilled_base') returns Spark NLP model_anno_obj camembert_embeddings_distilcamembert_base\n",
            "nlu.load('fr.embed.camembert.generic') returns Spark NLP model_anno_obj camembert_embeddings_doyyingface_generic_model\n",
            "nlu.load('fr.embed.camembert.generic.by_adam1224') returns Spark NLP model_anno_obj camembert_embeddings_adam1224_generic_model\n",
            "nlu.load('fr.embed.camembert.generic.by_aliasdasd') returns Spark NLP model_anno_obj camembert_embeddings_aliasdasd_generic_model\n",
            "nlu.load('fr.embed.camembert.generic.by_ankitkupadhyay') returns Spark NLP model_anno_obj camembert_embeddings_ankitkupadhyay_generic_model\n",
            "nlu.load('fr.embed.camembert.generic.by_codingjacob') returns Spark NLP model_anno_obj camembert_embeddings_codingjacob_generic_model\n",
            "nlu.load('fr.embed.camembert.generic.by_cylee') returns Spark NLP model_anno_obj camembert_embeddings_cylee_generic_model\n",
            "nlu.load('fr.embed.camembert.generic.by_devtrent') returns Spark NLP model_anno_obj camembert_embeddings_devtrent_generic_model\n",
            "nlu.load('fr.embed.camembert.generic.by_dianeshan') returns Spark NLP model_anno_obj camembert_embeddings_dianeshan_generic_model\n",
            "nlu.load('fr.embed.camembert.generic.by_edge2992') returns Spark NLP model_anno_obj camembert_embeddings_edge2992_generic_model\n",
            "nlu.load('fr.embed.camembert.generic.by_eduardopds') returns Spark NLP model_anno_obj camembert_embeddings_eduardopds_generic_model\n",
            "nlu.load('fr.embed.camembert.generic.by_elliotsmith') returns Spark NLP model_anno_obj camembert_embeddings_elliotsmith_generic_model\n",
            "nlu.load('fr.embed.camembert.generic.by_elusive_magnolia') returns Spark NLP model_anno_obj camembert_embeddings_elusive_magnolia_generic_model\n",
            "nlu.load('fr.embed.camembert.generic.by_ericchchiu') returns Spark NLP model_anno_obj camembert_embeddings_ericchchiu_generic_model\n",
            "nlu.load('fr.embed.camembert.generic.by_fjluque') returns Spark NLP model_anno_obj camembert_embeddings_fjluque_generic_model\n",
            "nlu.load('fr.embed.camembert.generic.by_gulabpatel') returns Spark NLP model_anno_obj camembert_embeddings_new_generic_model\n",
            "nlu.load('fr.embed.camembert.generic.by_h4d35') returns Spark NLP model_anno_obj camembert_embeddings_h4d35_generic_model\n",
            "nlu.load('fr.embed.camembert.generic.by_hackertec') returns Spark NLP model_anno_obj camembert_embeddings_generic2\n",
            "nlu.load('fr.embed.camembert.generic.by_hasanmurad') returns Spark NLP model_anno_obj camembert_embeddings_hasanmurad_generic_model\n",
            "nlu.load('fr.embed.camembert.generic.by_hasanmuradbuet') returns Spark NLP model_anno_obj camembert_embeddings_hasanmuradbuet_generic_model\n",
            "nlu.load('fr.embed.camembert.generic.by_henrywang') returns Spark NLP model_anno_obj camembert_embeddings_henrywang_generic_model\n",
            "nlu.load('fr.embed.camembert.generic.by_jcai1') returns Spark NLP model_anno_obj camembert_embeddings_jcai1_generic_model\n",
            "nlu.load('fr.embed.camembert.generic.by_joe8zhang') returns Spark NLP model_anno_obj camembert_embeddings_joe8zhang_generic_model\n",
            "nlu.load('fr.embed.camembert.generic.by_jonathansum') returns Spark NLP model_anno_obj camembert_embeddings_jonathansum_generic_model\n",
            "nlu.load('fr.embed.camembert.generic.by_juliencarbonnell') returns Spark NLP model_anno_obj camembert_embeddings_juliencarbonnell_generic_model\n",
            "nlu.load('fr.embed.camembert.generic.by_katrin_kc') returns Spark NLP model_anno_obj camembert_embeddings_katrin_kc_generic_model\n",
            "nlu.load('fr.embed.camembert.generic.by_katster') returns Spark NLP model_anno_obj camembert_embeddings_katster_generic_model\n",
            "nlu.load('fr.embed.camembert.generic.by_kaushikacharya') returns Spark NLP model_anno_obj camembert_embeddings_kaushikacharya_generic_model\n",
            "nlu.load('fr.embed.camembert.generic.by_leisa') returns Spark NLP model_anno_obj camembert_embeddings_leisa_generic_model\n",
            "nlu.load('fr.embed.camembert.generic.by_lewtun') returns Spark NLP model_anno_obj camembert_embeddings_lewtun_generic_model\n",
            "nlu.load('fr.embed.camembert.generic.by_lijingxin') returns Spark NLP model_anno_obj camembert_embeddings_lijingxin_generic_model\n",
            "nlu.load('fr.embed.camembert.generic.by_linyi') returns Spark NLP model_anno_obj camembert_embeddings_linyi_generic_model\n",
            "nlu.load('fr.embed.camembert.generic.by_mbateman') returns Spark NLP model_anno_obj camembert_embeddings_mbateman_generic_model\n",
            "nlu.load('fr.embed.camembert.generic.by_mohammadrea76') returns Spark NLP model_anno_obj camembert_embeddings_mohammadrea76_generic_model\n",
            "nlu.load('fr.embed.camembert.generic.by_myx4567') returns Spark NLP model_anno_obj camembert_embeddings_myx4567_generic_model\n",
            "nlu.load('fr.embed.camembert.generic.by_osanseviero') returns Spark NLP model_anno_obj camembert_embeddings_generic_model_test\n",
            "nlu.load('fr.embed.camembert.generic.by_peterhsu') returns Spark NLP model_anno_obj camembert_embeddings_peterhsu_generic_model\n",
            "nlu.load('fr.embed.camembert.generic.by_pgperrone') returns Spark NLP model_anno_obj camembert_embeddings_pgperrone_generic_model\n",
            "nlu.load('fr.embed.camembert.generic.by_safik') returns Spark NLP model_anno_obj camembert_embeddings_safik_generic_model\n",
            "nlu.load('fr.embed.camembert.generic.by_sebu') returns Spark NLP model_anno_obj camembert_embeddings_sebu_generic_model\n",
            "nlu.load('fr.embed.camembert.generic.by_seyfullah') returns Spark NLP model_anno_obj camembert_embeddings_seyfullah_generic_model\n",
            "nlu.load('fr.embed.camembert.generic.by_sonny') returns Spark NLP model_anno_obj camembert_embeddings_sonny_generic_model\n",
            "nlu.load('fr.embed.camembert.generic.by_tnagata') returns Spark NLP model_anno_obj camembert_embeddings_tnagata_generic_model\n",
            "nlu.load('fr.embed.camembert.generic.by_tpanza') returns Spark NLP model_anno_obj camembert_embeddings_tpanza_generic_model\n",
            "nlu.load('fr.embed.camembert.generic.by_wangst') returns Spark NLP model_anno_obj camembert_embeddings_wangst_generic_model\n",
            "nlu.load('fr.embed.camembert.generic.by_weipeng') returns Spark NLP model_anno_obj camembert_embeddings_weipeng_generic_model\n",
            "nlu.load('fr.embed.camembert.generic.by_xkang') returns Spark NLP model_anno_obj camembert_embeddings_xkang_generic_model\n",
            "nlu.load('fr.embed.camembert.generic.by_yancong') returns Spark NLP model_anno_obj camembert_embeddings_yancong_generic_model\n",
            "nlu.load('fr.embed.camembert.generic.by_ysharma') returns Spark NLP model_anno_obj camembert_embeddings_ysharma_generic_model_2\n",
            "nlu.load('fr.embed.camembert.generic.by_zhenghuabin') returns Spark NLP model_anno_obj camembert_embeddings_zhenghuabin_generic_model\n",
            "nlu.load('fr.embed.camembert.generic_v2.by_fjluque') returns Spark NLP model_anno_obj camembert_embeddings_fjluque_generic_model2\n",
            "nlu.load('fr.embed.camembert.generic_v2.by_hackertec') returns Spark NLP model_anno_obj camembert_embeddings_hackertec_generic\n",
            "nlu.load('fr.embed.camembert.generic_v2.by_lijingxin') returns Spark NLP model_anno_obj camembert_embeddings_lijingxin_generic_model_2\n",
            "nlu.load('fr.embed.camembert.generic_v2.by_osanseviero') returns Spark NLP model_anno_obj camembert_embeddings_osanseviero_generic_model\n",
            "nlu.load('fr.embed.camembert.generic_v2.by_peterhsu') returns Spark NLP model_anno_obj camembert_embeddings_tf_generic_model\n",
            "nlu.load('fr.embed.camembert.tweet.base') returns Spark NLP model_anno_obj camembert_embeddings_bertweetfr_base\n",
            "nlu.load('fr.embed.camembert_base') returns Spark NLP model_anno_obj camembert_base\n",
            "nlu.load('fr.embed.camembert_base_ccnet') returns Spark NLP model_anno_obj camembert_base_ccnet\n",
            "nlu.load('fr.embed.camembert_ccnet4g') returns Spark NLP model_anno_obj camembert_base_ccnet_4gb\n",
            "nlu.load('fr.embed.camembert_large') returns Spark NLP model_anno_obj camembert_large\n",
            "nlu.load('fr.embed.camembert_oscar_4g') returns Spark NLP model_anno_obj camembert_base_oscar_4gb\n",
            "nlu.load('fr.embed.camembert_wiki_4g') returns Spark NLP model_anno_obj camembert_base_wikipedia_4gb\n",
            "nlu.load('fr.embed.distilbert') returns Spark NLP model_anno_obj distilbert_embeddings_distilbert_base_fr_cased\n",
            "nlu.load('fr.embed.electra.cased_base') returns Spark NLP model_anno_obj electra_embeddings_electra_base_french_europeana_cased_generator\n",
            "nlu.load('fr.embed.french_roberta') returns Spark NLP model_anno_obj roberta_embeddings_french_roberta\n",
            "nlu.load('fr.embed.roberta_base_wechsel_french') returns Spark NLP model_anno_obj roberta_embeddings_roberta_base_wechsel_french\n",
            "nlu.load('fr.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "nlu.load('fr.embed.word2vec_wac_200') returns Spark NLP model_anno_obj word2vec_wac_200\n",
            "nlu.load('fr.embed.word2vec_wiki_1000') returns Spark NLP model_anno_obj word2vec_wiki_1000\n",
            "For language <frr> NLU provides the following Models : \n",
            "nlu.load('frr.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <fy> NLU provides the following Models : \n",
            "nlu.load('fy.embed.bert.cased_base') returns Spark NLP model_anno_obj bert_embeddings_base_dutch_cased_frisian\n",
            "nlu.load('fy.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <gd> NLU provides the following Models : \n",
            "nlu.load('gd.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <gl> NLU provides the following Models : \n",
            "nlu.load('gl.embed.roberta') returns Spark NLP model_anno_obj roberta_embeddings_robertinh\n",
            "nlu.load('gl.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <gom> NLU provides the following Models : \n",
            "nlu.load('gom.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <gu> NLU provides the following Models : \n",
            "nlu.load('gu.embed.RoBERTa_hindi_guj_san') returns Spark NLP model_anno_obj roberta_embeddings_RoBERTa_hindi_guj_san\n",
            "For language <gv> NLU provides the following Models : \n",
            "nlu.load('gv.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <ha> NLU provides the following Models : \n",
            "nlu.load('ha.embed.bert.cased_multilingual_base_finetuned') returns Spark NLP model_anno_obj bert_embeddings_base_multilingual_cased_finetuned_hausa\n",
            "nlu.load('ha.embed.bert.cased_multilingual_base_finetuned.by_davlan') returns Spark NLP model_anno_obj bert_embeddings_base_multilingual_cased_finetuned_swahili\n",
            "nlu.load('ha.embed.xlm_roberta') returns Spark NLP model_anno_obj xlm_roberta_base_finetuned_hausa\n",
            "For language <he> NLU provides the following Models : \n",
            "nlu.load('he.embed') returns Spark NLP model_anno_obj hebrew_cc_300d\n",
            "nlu.load('he.embed.bert.base') returns Spark NLP model_anno_obj bert_embeddings_onlplab_aleph_base\n",
            "nlu.load('he.embed.bert.legal') returns Spark NLP model_anno_obj bert_embeddings_legal_hebert\n",
            "nlu.load('he.embed.bert.legal.by_avichr') returns Spark NLP model_anno_obj bert_embeddings_legal_hebert_ft\n",
            "nlu.load('he.embed.cbow_300d') returns Spark NLP model_anno_obj hebrew_cc_300d\n",
            "nlu.load('he.embed.glove') returns Spark NLP model_anno_obj hebrew_cc_300d\n",
            "For language <hi> NLU provides the following Models : \n",
            "nlu.load('hi.embed') returns Spark NLP model_anno_obj hindi_cc_300d\n",
            "nlu.load('hi.embed.RoBERTa_hindi_guj_san') returns Spark NLP model_anno_obj roberta_embeddings_RoBERTa_hindi_guj_san\n",
            "nlu.load('hi.embed.bert') returns Spark NLP model_anno_obj bert_embeddings_indic_transformers\n",
            "nlu.load('hi.embed.bert.cased_base') returns Spark NLP model_anno_obj bert_embeddings_base_hi_cased\n",
            "nlu.load('hi.embed.bert_hi_cased') returns Spark NLP model_anno_obj bert_embeddings_bert_base_hi_cased\n",
            "nlu.load('hi.embed.distil_bert') returns Spark NLP model_anno_obj distilbert_embeddings_indic_transformers\n",
            "nlu.load('hi.embed.distilbert_base_hi_cased') returns Spark NLP model_anno_obj distilbert_embeddings_distilbert_base_hi_cased\n",
            "nlu.load('hi.embed.indic_transformers_hi_bert') returns Spark NLP model_anno_obj bert_embeddings_indic_transformers_hi_bert\n",
            "nlu.load('hi.embed.indic_transformers_hi_distilbert') returns Spark NLP model_anno_obj distilbert_embeddings_indic_transformers_hi_distilbert\n",
            "nlu.load('hi.embed.indic_transformers_hi_roberta') returns Spark NLP model_anno_obj roberta_embeddings_indic_transformers_hi_roberta\n",
            "nlu.load('hi.embed.muril_adapted_local') returns Spark NLP model_anno_obj bert_embeddings_muril_adapted_local\n",
            "nlu.load('hi.embed.roberta') returns Spark NLP model_anno_obj roberta_embeddings_hindi\n",
            "nlu.load('hi.embed.roberta.by_neuralspace_reverie') returns Spark NLP model_anno_obj roberta_embeddings_indic_transformers\n",
            "nlu.load('hi.embed.xlmr_roberta') returns Spark NLP model_anno_obj xlmroberta_embeddings_indic_transformers_hi_xlmroberta\n",
            "For language <hif> NLU provides the following Models : \n",
            "nlu.load('hif.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <hr> NLU provides the following Models : \n",
            "nlu.load('hr.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <hsb> NLU provides the following Models : \n",
            "nlu.load('hsb.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <hy> NLU provides the following Models : \n",
            "nlu.load('hy.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <id> NLU provides the following Models : \n",
            "nlu.load('id.embed.bert.base') returns Spark NLP model_anno_obj bert_embeddings_base_indonesian_1.5g\n",
            "nlu.load('id.embed.bert.base_522m') returns Spark NLP model_anno_obj bert_embeddings_base_indonesian_522m\n",
            "nlu.load('id.embed.distilbert') returns Spark NLP model_anno_obj distilbert_embeddings_distilbert_base_indonesian\n",
            "nlu.load('id.embed.indo_roberta_small') returns Spark NLP model_anno_obj roberta_embeddings_indo_roberta_small\n",
            "nlu.load('id.embed.indonesian_roberta_base') returns Spark NLP model_anno_obj roberta_embeddings_indonesian_roberta_base\n",
            "nlu.load('id.embed.indonesian_roberta_large') returns Spark NLP model_anno_obj roberta_embeddings_indonesian_roberta_large\n",
            "nlu.load('id.embed.roberta.base_522m') returns Spark NLP model_anno_obj roberta_embeddings_base_indonesian_522m\n",
            "nlu.load('id.embed.roberta.small') returns Spark NLP model_anno_obj roberta_embeddings_indo_small\n",
            "nlu.load('id.embed.roberta_base_indonesian_522M') returns Spark NLP model_anno_obj roberta_embeddings_roberta_base_indonesian_522M\n",
            "For language <ig> NLU provides the following Models : \n",
            "nlu.load('ig.embed.xlm_roberta') returns Spark NLP model_anno_obj xlm_roberta_base_finetuned_igbo\n",
            "For language <it> NLU provides the following Models : \n",
            "nlu.load('it.embed.BERTino') returns Spark NLP model_anno_obj distilbert_embeddings_BERTino\n",
            "nlu.load('it.embed.bert') returns Spark NLP model_anno_obj bert_base_italian_cased\n",
            "nlu.load('it.embed.bert.cased_base') returns Spark NLP model_anno_obj bert_embeddings_base_it_cased\n",
            "nlu.load('it.embed.bert.cased_base.by_dbmdz') returns Spark NLP model_anno_obj bert_embeddings_base_italian_cased\n",
            "nlu.load('it.embed.bert.cased_xxl_base') returns Spark NLP model_anno_obj bert_embeddings_base_italian_xxl_cased\n",
            "nlu.load('it.embed.bert.uncased') returns Spark NLP model_anno_obj bert_base_italian_uncased\n",
            "nlu.load('it.embed.bert.uncased_base') returns Spark NLP model_anno_obj bert_embeddings_base_italian_uncased\n",
            "nlu.load('it.embed.bert.uncased_xxl_base') returns Spark NLP model_anno_obj bert_embeddings_base_italian_xxl_uncased\n",
            "nlu.load('it.embed.bert_base_italian_xxl_cased') returns Spark NLP model_anno_obj bert_embeddings_bert_base_italian_xxl_cased\n",
            "nlu.load('it.embed.bert_base_italian_xxl_uncased') returns Spark NLP model_anno_obj bert_embeddings_bert_base_italian_xxl_uncased\n",
            "nlu.load('it.embed.bert_it_cased') returns Spark NLP model_anno_obj bert_embeddings_bert_base_it_cased\n",
            "nlu.load('it.embed.camembert.cased') returns Spark NLP model_anno_obj camembert_embeddings_umberto_commoncrawl_cased_v1\n",
            "nlu.load('it.embed.camembert.uncased') returns Spark NLP model_anno_obj camembert_embeddings_umberto_wikipedia_uncased_v1\n",
            "nlu.load('it.embed.chefberto_italian_cased') returns Spark NLP model_anno_obj bert_embeddings_chefberto_italian_cased\n",
            "nlu.load('it.embed.distil_bert') returns Spark NLP model_anno_obj distilbert_embeddings_bertino\n",
            "nlu.load('it.embed.distilbert_base_it_cased') returns Spark NLP model_anno_obj distilbert_embeddings_distilbert_base_it_cased\n",
            "nlu.load('it.embed.electra.cased_xxl_base') returns Spark NLP model_anno_obj electra_embeddings_electra_base_italian_xxl_cased_generator\n",
            "nlu.load('it.embed.hseBert_it_cased') returns Spark NLP model_anno_obj bert_embeddings_hseBert_it_cased\n",
            "nlu.load('it.embed.wineberto_italian_cased') returns Spark NLP model_anno_obj bert_embeddings_wineberto_italian_cased\n",
            "nlu.load('it.embed.word2vec') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <ja> NLU provides the following Models : \n",
            "nlu.load('ja.embed.albert_base_japanese_v1') returns Spark NLP model_anno_obj albert_embeddings_albert_base_japanese_v1\n",
            "nlu.load('ja.embed.bert.base') returns Spark NLP model_anno_obj bert_base_japanese\n",
            "nlu.load('ja.embed.bert.base_whole_word_masking') returns Spark NLP model_anno_obj bert_embeddings_base_japanese_char_whole_word_masking\n",
            "nlu.load('ja.embed.bert.base_whole_word_masking.by_cl_tohoku') returns Spark NLP model_anno_obj bert_embeddings_base_japanese_whole_word_masking\n",
            "nlu.load('ja.embed.bert.cased_base') returns Spark NLP model_anno_obj bert_embeddings_base_ja_cased\n",
            "nlu.load('ja.embed.bert.large') returns Spark NLP model_anno_obj bert_embeddings_large_japanese\n",
            "nlu.load('ja.embed.bert.large.by_cl_tohoku') returns Spark NLP model_anno_obj bert_embeddings_large_japanese_char\n",
            "nlu.load('ja.embed.bert.v2_base') returns Spark NLP model_anno_obj bert_embeddings_base_japanese_char_v2\n",
            "nlu.load('ja.embed.bert.v2_base.by_cl_tohoku') returns Spark NLP model_anno_obj bert_embeddings_base_japanese_v2\n",
            "nlu.load('ja.embed.bert.wiki.base.by_cl_tohoku') returns Spark NLP model_anno_obj bert_embeddings_base_japanese\n",
            "nlu.load('ja.embed.bert.wiki.base_char.by_cl_tohoku') returns Spark NLP model_anno_obj bert_embeddings_base_japanese_char\n",
            "nlu.load('ja.embed.bert_base_ja_cased') returns Spark NLP model_anno_obj bert_embeddings_bert_base_ja_cased\n",
            "nlu.load('ja.embed.bert_base_japanese_basic_char_v2') returns Spark NLP model_anno_obj bert_embeddings_bert_base_japanese_basic_char_v2\n",
            "nlu.load('ja.embed.bert_base_japanese_char') returns Spark NLP model_anno_obj bert_embeddings_bert_base_japanese_char\n",
            "nlu.load('ja.embed.bert_base_japanese_char_extended') returns Spark NLP model_anno_obj bert_embeddings_bert_base_japanese_char_extended\n",
            "nlu.load('ja.embed.bert_base_japanese_char_v2') returns Spark NLP model_anno_obj bert_embeddings_bert_base_japanese_char_v2\n",
            "nlu.load('ja.embed.bert_base_japanese_char_whole_word_masking') returns Spark NLP model_anno_obj bert_embeddings_bert_base_japanese_char_whole_word_masking\n",
            "nlu.load('ja.embed.bert_base_japanese_v2') returns Spark NLP model_anno_obj bert_embeddings_bert_base_japanese_v2\n",
            "nlu.load('ja.embed.bert_base_japanese_whole_word_masking') returns Spark NLP model_anno_obj bert_embeddings_bert_base_japanese_whole_word_masking\n",
            "nlu.load('ja.embed.bert_large_japanese') returns Spark NLP model_anno_obj bert_embeddings_bert_large_japanese\n",
            "nlu.load('ja.embed.bert_large_japanese_char') returns Spark NLP model_anno_obj bert_embeddings_bert_large_japanese_char\n",
            "nlu.load('ja.embed.bert_large_japanese_char_extended') returns Spark NLP model_anno_obj bert_embeddings_bert_large_japanese_char_extended\n",
            "nlu.load('ja.embed.bert_small_japanese') returns Spark NLP model_anno_obj bert_embeddings_bert_small_japanese\n",
            "nlu.load('ja.embed.bert_small_japanese_fin') returns Spark NLP model_anno_obj bert_embeddings_bert_small_japanese_fin\n",
            "nlu.load('ja.embed.distilbert_base_ja_cased') returns Spark NLP model_anno_obj distilbert_embeddings_distilbert_base_ja_cased\n",
            "nlu.load('ja.embed.electra.base') returns Spark NLP model_anno_obj electra_embeddings_electra_base_japanese_generator\n",
            "nlu.load('ja.embed.electra.small') returns Spark NLP model_anno_obj electra_embeddings_electra_small_japanese_fin_generator\n",
            "nlu.load('ja.embed.electra.small.by_cinnamon') returns Spark NLP model_anno_obj electra_embeddings_electra_small_japanese_generator\n",
            "nlu.load('ja.embed.electra.small_paper_japanese_fin_generator.small.by_izumi_lab') returns Spark NLP model_anno_obj electra_embeddings_electra_small_paper_japanese_fin_generator\n",
            "nlu.load('ja.embed.electra.small_paper_japanese_generator.small.by_izumi_lab') returns Spark NLP model_anno_obj electra_embeddings_electra_small_paper_japanese_generator\n",
            "nlu.load('ja.embed.glove.cc_300d') returns Spark NLP model_anno_obj japanese_cc_300d\n",
            "For language <jv> NLU provides the following Models : \n",
            "nlu.load('jv.embed.bert.imdb_javanese.small') returns Spark NLP model_anno_obj bert_embeddings_javanese_small_imdb\n",
            "nlu.load('jv.embed.bert.small') returns Spark NLP model_anno_obj bert_embeddings_javanese_small\n",
            "nlu.load('jv.embed.distil_bert.imdb_javanese.small') returns Spark NLP model_anno_obj distilbert_embeddings_javanese_small_imdb\n",
            "nlu.load('jv.embed.distil_bert.small') returns Spark NLP model_anno_obj distilbert_embeddings_javanese_small\n",
            "nlu.load('jv.embed.distilbert') returns Spark NLP model_anno_obj distilbert_embeddings_javanese_distilbert_small\n",
            "nlu.load('jv.embed.javanese_bert_small') returns Spark NLP model_anno_obj bert_embeddings_javanese_bert_small\n",
            "nlu.load('jv.embed.javanese_bert_small_imdb') returns Spark NLP model_anno_obj bert_embeddings_javanese_bert_small_imdb\n",
            "nlu.load('jv.embed.javanese_distilbert_small_imdb') returns Spark NLP model_anno_obj distilbert_embeddings_javanese_distilbert_small_imdb\n",
            "nlu.load('jv.embed.javanese_roberta_small') returns Spark NLP model_anno_obj roberta_embeddings_javanese_roberta_small\n",
            "nlu.load('jv.embed.javanese_roberta_small_imdb') returns Spark NLP model_anno_obj roberta_embeddings_javanese_roberta_small_imdb\n",
            "nlu.load('jv.embed.roberta.imdb_javanese.small') returns Spark NLP model_anno_obj roberta_embeddings_javanese_small_imdb\n",
            "nlu.load('jv.embed.roberta.small') returns Spark NLP model_anno_obj roberta_embeddings_javanese_small\n",
            "For language <ka> NLU provides the following Models : \n",
            "nlu.load('ka.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <kn> NLU provides the following Models : \n",
            "nlu.load('kn.embed.KNUBert') returns Spark NLP model_anno_obj roberta_embeddings_KNUBert\n",
            "nlu.load('kn.embed.KanBERTo') returns Spark NLP model_anno_obj roberta_embeddings_KanBERTo\n",
            "For language <ko> NLU provides the following Models : \n",
            "nlu.load('ko.embed.KR_FinBert') returns Spark NLP model_anno_obj bert_embeddings_KR_FinBert\n",
            "nlu.load('ko.embed.bert') returns Spark NLP model_anno_obj bert_embeddings_bert_base\n",
            "nlu.load('ko.embed.bert.base') returns Spark NLP model_anno_obj bert_embeddings_kor_base\n",
            "nlu.load('ko.embed.bert_base_v1_sports') returns Spark NLP model_anno_obj bert_embeddings_bert_base_v1_sports\n",
            "nlu.load('ko.embed.bert_kor_base') returns Spark NLP model_anno_obj bert_embeddings_bert_kor_base\n",
            "nlu.load('ko.embed.dbert') returns Spark NLP model_anno_obj bert_embeddings_dbert\n",
            "nlu.load('ko.embed.electra') returns Spark NLP model_anno_obj electra_embeddings_kr_electra_generator\n",
            "nlu.load('ko.embed.electra.base') returns Spark NLP model_anno_obj electra_embeddings_finance_koelectra_base_generator\n",
            "nlu.load('ko.embed.electra.by_deeq') returns Spark NLP model_anno_obj electra_embeddings_delectra_generator\n",
            "nlu.load('ko.embed.electra.small') returns Spark NLP model_anno_obj electra_embeddings_finance_koelectra_small_generator\n",
            "nlu.load('ko.embed.electra.small.by_monologg') returns Spark NLP model_anno_obj electra_embeddings_koelectra_small_generator\n",
            "nlu.load('ko.embed.electra.v2_base') returns Spark NLP model_anno_obj electra_embeddings_koelectra_base_v2_generator\n",
            "nlu.load('ko.embed.koelelectra.base.by_monologg') returns Spark NLP model_anno_obj electra_embeddings_koelectra_base_generator\n",
            "nlu.load('ko.embed.koelelectra.base_v3.by_monologg') returns Spark NLP model_anno_obj electra_embeddings_koelectra_base_v3_generator\n",
            "nlu.load('ko.embed.roberta_ko_small') returns Spark NLP model_anno_obj roberta_embeddings_roberta_ko_small\n",
            "For language <la> NLU provides the following Models : \n",
            "nlu.load('la.embed.bert') returns Spark NLP model_anno_obj bert_embeddings_cicero_similis\n",
            "For language <lb> NLU provides the following Models : \n",
            "nlu.load('lb.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <lg> NLU provides the following Models : \n",
            "nlu.load('lg.embed.xlm_roberta') returns Spark NLP model_anno_obj xlm_roberta_base_finetuned_luganda\n",
            "For language <lmo> NLU provides the following Models : \n",
            "nlu.load('lmo.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <lou> NLU provides the following Models : \n",
            "nlu.load('lou.embed.xlm_roberta') returns Spark NLP model_anno_obj xlm_roberta_base_finetuned_luo\n",
            "For language <lt> NLU provides the following Models : \n",
            "nlu.load('lt.embed.bert.cased_base') returns Spark NLP model_anno_obj bert_embeddings_base_lt_cased\n",
            "nlu.load('lt.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <lu> NLU provides the following Models : \n",
            "nlu.load('lu.embed.bert.medium') returns Spark NLP model_anno_obj bert_embeddings_medium_luxembourgish\n",
            "For language <mai> NLU provides the following Models : \n",
            "nlu.load('mai.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <mg> NLU provides the following Models : \n",
            "nlu.load('mg.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <min> NLU provides the following Models : \n",
            "nlu.load('min.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <mk> NLU provides the following Models : \n",
            "nlu.load('mk.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <ml> NLU provides the following Models : \n",
            "nlu.load('ml.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <mn> NLU provides the following Models : \n",
            "nlu.load('mn.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <mr> NLU provides the following Models : \n",
            "nlu.load('mr.embed.albert') returns Spark NLP model_anno_obj albert_embeddings_marathi_albert\n",
            "nlu.load('mr.embed.albert_v2') returns Spark NLP model_anno_obj albert_embeddings_marathi_albert_v2\n",
            "nlu.load('mr.embed.distil_bert') returns Spark NLP model_anno_obj distilbert_embeddings_marathi\n",
            "nlu.load('mr.embed.distilbert') returns Spark NLP model_anno_obj distilbert_embeddings_marathi_distilbert\n",
            "nlu.load('mr.embed.marathi_bert') returns Spark NLP model_anno_obj bert_embeddings_marathi_bert\n",
            "nlu.load('mr.embed.muril_adapted_local') returns Spark NLP model_anno_obj bert_embeddings_muril_adapted_local\n",
            "nlu.load('mr.embed.xlmr_roberta') returns Spark NLP model_anno_obj xlmroberta_embeddings_marathi_roberta\n",
            "For language <ms> NLU provides the following Models : \n",
            "nlu.load('ms.embed.albert') returns Spark NLP model_anno_obj albert_embeddings_albert_large_bahasa_cased\n",
            "nlu.load('ms.embed.albert_base_bahasa_cased') returns Spark NLP model_anno_obj albert_embeddings_albert_base_bahasa_cased\n",
            "nlu.load('ms.embed.albert_tiny_bahasa_cased') returns Spark NLP model_anno_obj albert_embeddings_albert_tiny_bahasa_cased\n",
            "nlu.load('ms.embed.bert') returns Spark NLP model_anno_obj bert_embeddings_melayubert\n",
            "nlu.load('ms.embed.distil_bert.small') returns Spark NLP model_anno_obj distilbert_embeddings_malaysian_small\n",
            "nlu.load('ms.embed.distilbert') returns Spark NLP model_anno_obj distilbert_embeddings_malaysian_distilbert_small\n",
            "nlu.load('ms.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <mt> NLU provides the following Models : \n",
            "nlu.load('mt.embed.camembert') returns Spark NLP model_anno_obj camembert_embeddings_camembert_aux_amandes\n",
            "nlu.load('mt.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <mwl> NLU provides the following Models : \n",
            "nlu.load('mwl.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <my> NLU provides the following Models : \n",
            "nlu.load('my.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <myv> NLU provides the following Models : \n",
            "nlu.load('myv.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <mzn> NLU provides the following Models : \n",
            "nlu.load('mzn.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <nah> NLU provides the following Models : \n",
            "nlu.load('nah.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <nap> NLU provides the following Models : \n",
            "nlu.load('nap.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <nds> NLU provides the following Models : \n",
            "nlu.load('nds.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <ne> NLU provides the following Models : \n",
            "nlu.load('ne.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <new> NLU provides the following Models : \n",
            "nlu.load('new.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <nl> NLU provides the following Models : \n",
            "nlu.load('nl.embed') returns Spark NLP model_anno_obj dutch_cc_300d\n",
            "nlu.load('nl.embed.bert') returns Spark NLP model_anno_obj bert_base_dutch_cased\n",
            "nlu.load('nl.embed.bert.base_cased') returns Spark NLP model_anno_obj bert_base_cased\n",
            "nlu.load('nl.embed.bert.cased_base') returns Spark NLP model_anno_obj bert_embeddings_base_dutch_cased\n",
            "nlu.load('nl.embed.bert.cased_base.by_geotrend') returns Spark NLP model_anno_obj bert_embeddings_base_nl_cased\n",
            "nlu.load('nl.embed.distilbert_base_cased') returns Spark NLP model_anno_obj distilbert_embeddings_distilbert_base_nl_cased\n",
            "nlu.load('nl.embed.robbert_v2_dutch_base') returns Spark NLP model_anno_obj roberta_embeddings_robbert_v2_dutch_base\n",
            "nlu.load('nl.embed.robbertje_1_gb_bort') returns Spark NLP model_anno_obj roberta_embeddings_robbertje_1_gb_bort\n",
            "nlu.load('nl.embed.robbertje_1_gb_merged') returns Spark NLP model_anno_obj roberta_embeddings_robbertje_1_gb_merged\n",
            "nlu.load('nl.embed.robbertje_1_gb_non_shuffled') returns Spark NLP model_anno_obj roberta_embeddings_robbertje_1_gb_non_shuffled\n",
            "nlu.load('nl.embed.robbertje_1_gb_shuffled') returns Spark NLP model_anno_obj roberta_embeddings_robbertje_1_gb_shuffled\n",
            "nlu.load('nl.embed.roberta') returns Spark NLP model_anno_obj roberta_embeddings_medroberta.nl\n",
            "nlu.load('nl.embed.roberta.conll.v2_base') returns Spark NLP model_anno_obj roberta_embeddings_pdelobelle_robbert_v2_dutch_base\n",
            "nlu.load('nl.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <nn> NLU provides the following Models : \n",
            "nlu.load('nn.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <no> NLU provides the following Models : \n",
            "nlu.load('no.embed.bert') returns Spark NLP model_anno_obj bert_embeddings_norbert\n",
            "nlu.load('no.embed.bert.by_ltgoslo') returns Spark NLP model_anno_obj bert_embeddings_norbert2\n",
            "nlu.load('no.embed.bert.cased_base') returns Spark NLP model_anno_obj bert_embeddings_base_no_cased\n",
            "nlu.load('no.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <nso> NLU provides the following Models : \n",
            "nlu.load('nso.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <oc> NLU provides the following Models : \n",
            "nlu.load('oc.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <or> NLU provides the following Models : \n",
            "nlu.load('or.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <os> NLU provides the following Models : \n",
            "nlu.load('os.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <pa> NLU provides the following Models : \n",
            "nlu.load('pa.embed.muril_adapted_local') returns Spark NLP model_anno_obj bert_embeddings_muril_adapted_local\n",
            "nlu.load('pa.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <pcm> NLU provides the following Models : \n",
            "nlu.load('pcm.embed.xlm_roberta') returns Spark NLP model_anno_obj xlm_roberta_base_finetuned_naija\n",
            "For language <pfl> NLU provides the following Models : \n",
            "nlu.load('pfl.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <pl> NLU provides the following Models : \n",
            "nlu.load('pl.embed.bert.cased_base') returns Spark NLP model_anno_obj bert_embeddings_base_pl_cased\n",
            "nlu.load('pl.embed.distilbert_base_cased') returns Spark NLP model_anno_obj distilbert_embeddings_distilbert_base_pl_cased\n",
            "nlu.load('pl.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <pms> NLU provides the following Models : \n",
            "nlu.load('pms.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <pnb> NLU provides the following Models : \n",
            "nlu.load('pnb.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <ps> NLU provides the following Models : \n",
            "nlu.load('ps.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <pt> NLU provides the following Models : \n",
            "nlu.load('pt.embed.BR_BERTo') returns Spark NLP model_anno_obj roberta_embeddings_BR_BERTo\n",
            "nlu.load('pt.embed.bert.cased_base') returns Spark NLP model_anno_obj bert_embeddings_base_portuguese_cased\n",
            "nlu.load('pt.embed.bert.cased_base.by_geotrend') returns Spark NLP model_anno_obj bert_embeddings_base_pt_cased\n",
            "nlu.load('pt.embed.bert_base_cased_pt_lenerbr') returns Spark NLP model_anno_obj bert_embeddings_bert_base_cased_pt_lenerbr\n",
            "nlu.load('pt.embed.bert_base_gl_cased') returns Spark NLP model_anno_obj bert_embeddings_bert_base_gl_cased\n",
            "nlu.load('pt.embed.bert_base_portuguese_cased') returns Spark NLP model_anno_obj bert_embeddings_bert_base_portuguese_cased\n",
            "nlu.load('pt.embed.bert_base_portuguese_cased_finetuned_peticoes') returns Spark NLP model_anno_obj bert_embeddings_bert_base_portuguese_cased_finetuned_peticoes\n",
            "nlu.load('pt.embed.bert_base_portuguese_cased_finetuned_tcu_acordaos') returns Spark NLP model_anno_obj bert_embeddings_bert_base_portuguese_cased_finetuned_tcu_acordaos\n",
            "nlu.load('pt.embed.bert_base_pt_cased') returns Spark NLP model_anno_obj bert_embeddings_bert_base_pt_cased\n",
            "nlu.load('pt.embed.bert_large_cased_pt_lenerbr') returns Spark NLP model_anno_obj bert_embeddings_bert_large_cased_pt_lenerbr\n",
            "nlu.load('pt.embed.bert_large_portuguese_cased') returns Spark NLP model_anno_obj bert_embeddings_bert_large_portuguese_cased\n",
            "nlu.load('pt.embed.bert_small_gl_cased') returns Spark NLP model_anno_obj bert_embeddings_bert_small_gl_cased\n",
            "nlu.load('pt.embed.biobert') returns Spark NLP model_anno_obj bert_embeddings_biobertpt_all\n",
            "nlu.load('pt.embed.biobert.by_pucpr') returns Spark NLP model_anno_obj bert_embeddings_biobertpt_bio\n",
            "nlu.load('pt.embed.biobert.clinical.by_pucpr') returns Spark NLP model_anno_obj bert_embeddings_biobertpt_clin\n",
            "nlu.load('pt.embed.distilbert_base_cased') returns Spark NLP model_anno_obj distilbert_embeddings_distilbert_base_pt_cased\n",
            "nlu.load('pt.embed.gs_all') returns Spark NLP model_anno_obj biobert_embeddings_all\n",
            "nlu.load('pt.embed.gs_biomedical') returns Spark NLP model_anno_obj biobert_embeddings_biomedical\n",
            "nlu.load('pt.embed.gs_clinical') returns Spark NLP model_anno_obj biobert_embeddings_clinical\n",
            "nlu.load('pt.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <qu> NLU provides the following Models : \n",
            "nlu.load('qu.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <rm> NLU provides the following Models : \n",
            "nlu.load('rm.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <ro> NLU provides the following Models : \n",
            "nlu.load('ro.embed.ALR_BERT') returns Spark NLP model_anno_obj albert_embeddings_ALR_BERT\n",
            "nlu.load('ro.embed.bert.cased_base') returns Spark NLP model_anno_obj bert_base_cased\n",
            "nlu.load('ro.embed.bert.cased_base.by_geotrend') returns Spark NLP model_anno_obj bert_embeddings_base_ro_cased\n",
            "nlu.load('ro.embed.distilbert_base_cased') returns Spark NLP model_anno_obj distilbert_embeddings_distilbert_base_ro_cased\n",
            "nlu.load('ro.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <ru> NLU provides the following Models : \n",
            "nlu.load('ru.embed.bert.cased_base') returns Spark NLP model_anno_obj bert_embeddings_base_ru_cased\n",
            "nlu.load('ru.embed.bert_base_ru_cased') returns Spark NLP model_anno_obj bert_embeddings_bert_base_ru_cased\n",
            "nlu.load('ru.embed.distilbert_base_cased') returns Spark NLP model_anno_obj distilbert_embeddings_distilbert_base_ru_cased\n",
            "nlu.load('ru.embed.roberta_base_russian_v0') returns Spark NLP model_anno_obj roberta_embeddings_roberta_base_russian_v0\n",
            "nlu.load('ru.embed.ruRoberta_large') returns Spark NLP model_anno_obj roberta_embeddings_ruRoberta_large\n",
            "nlu.load('ru.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <rw> NLU provides the following Models : \n",
            "nlu.load('rw.embed.xlm_roberta') returns Spark NLP model_anno_obj xlm_roberta_base_finetuned_kinyarwanda\n",
            "For language <sa> NLU provides the following Models : \n",
            "nlu.load('sa.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <sah> NLU provides the following Models : \n",
            "nlu.load('sah.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <sc> NLU provides the following Models : \n",
            "nlu.load('sc.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <scn> NLU provides the following Models : \n",
            "nlu.load('scn.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <sco> NLU provides the following Models : \n",
            "nlu.load('sco.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <sd> NLU provides the following Models : \n",
            "nlu.load('sd.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <sh> NLU provides the following Models : \n",
            "nlu.load('sh.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <si> NLU provides the following Models : \n",
            "nlu.load('si.embed.roberta') returns Spark NLP model_anno_obj roberta_embeddings_sinhalaberto\n",
            "nlu.load('si.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <sk> NLU provides the following Models : \n",
            "nlu.load('sk.embed.bert') returns Spark NLP model_anno_obj bert_embeddings_fernet_cc\n",
            "nlu.load('sk.embed.roberta') returns Spark NLP model_anno_obj roberta_embeddings_slovakbert\n",
            "nlu.load('sk.embed.roberta.news.') returns Spark NLP model_anno_obj roberta_embeddings_fernet_news\n",
            "nlu.load('sk.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <sl> NLU provides the following Models : \n",
            "nlu.load('sl.embed.camembert') returns Spark NLP model_anno_obj camembert_embeddings_sloberta\n",
            "nlu.load('sl.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <so> NLU provides the following Models : \n",
            "nlu.load('so.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <sq> NLU provides the following Models : \n",
            "nlu.load('sq.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <sr> NLU provides the following Models : \n",
            "nlu.load('sr.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <su> NLU provides the following Models : \n",
            "nlu.load('su.embed.sundanese_roberta_base') returns Spark NLP model_anno_obj roberta_embeddings_sundanese_roberta_base\n",
            "nlu.load('su.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <sv> NLU provides the following Models : \n",
            "nlu.load('sv.embed.bert.base_cased') returns Spark NLP model_anno_obj bert_base_cased\n",
            "nlu.load('sv.embed.bert.cased_base') returns Spark NLP model_anno_obj bert_embeddings_kb_base_swedish_cased\n",
            "nlu.load('sv.embed.bert.cased_base.by_kblab') returns Spark NLP model_anno_obj bert_embeddings_kblab_base_swedish_cased\n",
            "nlu.load('sv.embed.bert.distilled_cased') returns Spark NLP model_anno_obj bert_embeddings_kb_distilled_cased\n",
            "nlu.load('sv.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <sw> NLU provides the following Models : \n",
            "nlu.load('sw.embed.bert.cased_base') returns Spark NLP model_anno_obj bert_embeddings_base_sw_cased\n",
            "nlu.load('sw.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "nlu.load('sw.embed.xlm_roberta') returns Spark NLP model_anno_obj xlm_roberta_base_finetuned_swahili\n",
            "For language <ta> NLU provides the following Models : \n",
            "nlu.load('ta.embed.muril_adapted_local') returns Spark NLP model_anno_obj bert_embeddings_muril_adapted_local\n",
            "nlu.load('ta.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <te> NLU provides the following Models : \n",
            "nlu.load('te.embed.bert') returns Spark NLP model_anno_obj bert_embeddings_indic_transformers\n",
            "nlu.load('te.embed.distil_bert') returns Spark NLP model_anno_obj distilbert_embeddings_indic_transformers\n",
            "nlu.load('te.embed.distilbert') returns Spark NLP model_anno_obj distilbert_uncased\n",
            "nlu.load('te.embed.indic_transformers_te_bert') returns Spark NLP model_anno_obj bert_embeddings_indic_transformers_te_bert\n",
            "nlu.load('te.embed.indic_transformers_te_roberta') returns Spark NLP model_anno_obj roberta_embeddings_indic_transformers_te_roberta\n",
            "nlu.load('te.embed.muril_adapted_local') returns Spark NLP model_anno_obj bert_embeddings_muril_adapted_local\n",
            "nlu.load('te.embed.roberta') returns Spark NLP model_anno_obj roberta_embeddings_indic_transformers\n",
            "nlu.load('te.embed.telugu_bertu') returns Spark NLP model_anno_obj bert_embeddings_telugu_bertu\n",
            "nlu.load('te.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "nlu.load('te.embed.xlmr_roberta') returns Spark NLP model_anno_obj xlmroberta_embeddings_indic_transformers_te_xlmroberta\n",
            "For language <tg> NLU provides the following Models : \n",
            "nlu.load('tg.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <th> NLU provides the following Models : \n",
            "nlu.load('th.embed.bert.cased_base') returns Spark NLP model_anno_obj bert_embeddings_base_th_cased\n",
            "nlu.load('th.embed.distilbert_base_cased') returns Spark NLP model_anno_obj distilbert_embeddings_distilbert_base_th_cased\n",
            "nlu.load('th.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <tk> NLU provides the following Models : \n",
            "nlu.load('tk.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <tl> NLU provides the following Models : \n",
            "nlu.load('tl.embed.electra.cased_base') returns Spark NLP model_anno_obj electra_embeddings_electra_tagalog_base_cased_generator\n",
            "nlu.load('tl.embed.electra.cased_small') returns Spark NLP model_anno_obj electra_embeddings_electra_tagalog_small_cased_generator\n",
            "nlu.load('tl.embed.electra.uncased_base') returns Spark NLP model_anno_obj electra_embeddings_electra_tagalog_base_uncased_generator\n",
            "nlu.load('tl.embed.electra.uncased_small') returns Spark NLP model_anno_obj electra_embeddings_electra_tagalog_small_uncased_generator\n",
            "nlu.load('tl.embed.roberta.base') returns Spark NLP model_anno_obj roberta_embeddings_tagalog_base\n",
            "nlu.load('tl.embed.roberta.large') returns Spark NLP model_anno_obj roberta_embeddings_tagalog_large\n",
            "nlu.load('tl.embed.roberta_tagalog_base') returns Spark NLP model_anno_obj roberta_embeddings_roberta_tagalog_base\n",
            "nlu.load('tl.embed.roberta_tagalog_large') returns Spark NLP model_anno_obj roberta_embeddings_roberta_tagalog_large\n",
            "nlu.load('tl.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <tn> NLU provides the following Models : \n",
            "nlu.load('tn.embed.roberta') returns Spark NLP model_anno_obj roberta_embeddings_tswanabert\n",
            "For language <tr> NLU provides the following Models : \n",
            "nlu.load('tr.embed.bert') returns Spark NLP model_anno_obj bert_base_turkish_cased\n",
            "nlu.load('tr.embed.bert.cased_base') returns Spark NLP model_anno_obj bert_embeddings_base_tr_cased\n",
            "nlu.load('tr.embed.bert.uncased') returns Spark NLP model_anno_obj bert_base_turkish_uncased\n",
            "nlu.load('tr.embed.bert_cased') returns Spark NLP model_anno_obj bert_embeddings_bert_base_tr_cased\n",
            "nlu.load('tr.embed.distilbert_base_cased') returns Spark NLP model_anno_obj distilbert_embeddings_distilbert_base_tr_cased\n",
            "nlu.load('tr.embed.electra.cased_base') returns Spark NLP model_anno_obj electra_embeddings_electra_base_turkish_mc4_cased_generator\n",
            "nlu.load('tr.embed.electra.uncased_base') returns Spark NLP model_anno_obj electra_embeddings_electra_base_turkish_mc4_uncased_generator\n",
            "nlu.load('tr.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <tt> NLU provides the following Models : \n",
            "nlu.load('tt.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <ug> NLU provides the following Models : \n",
            "nlu.load('ug.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <uk> NLU provides the following Models : \n",
            "nlu.load('uk.embed.bert.cased_base') returns Spark NLP model_anno_obj bert_embeddings_base_uk_cased\n",
            "nlu.load('uk.embed.distilbert_base_cased') returns Spark NLP model_anno_obj distilbert_embeddings_distilbert_base_uk_cased\n",
            "nlu.load('uk.embed.ukr_roberta_base') returns Spark NLP model_anno_obj roberta_embeddings_ukr_roberta_base\n",
            "nlu.load('uk.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "nlu.load('uk.embed.xlmr_roberta.base') returns Spark NLP model_anno_obj xlmroberta_embeddings_xlm_roberta_base\n",
            "For language <ur> NLU provides the following Models : \n",
            "nlu.load('ur.embed') returns Spark NLP model_anno_obj urduvec_140M_300d\n",
            "nlu.load('ur.embed.bert.cased_base') returns Spark NLP model_anno_obj bert_embeddings_base_ur_cased\n",
            "nlu.load('ur.embed.bert_cased') returns Spark NLP model_anno_obj bert_embeddings_bert_base_ur_cased\n",
            "nlu.load('ur.embed.distilbert_base_cased') returns Spark NLP model_anno_obj distilbert_embeddings_distilbert_base_ur_cased\n",
            "nlu.load('ur.embed.glove.300d') returns Spark NLP model_anno_obj urduvec_140M_300d\n",
            "nlu.load('ur.embed.muril_adapted_local') returns Spark NLP model_anno_obj bert_embeddings_muril_adapted_local\n",
            "nlu.load('ur.embed.roberta_urdu_small') returns Spark NLP model_anno_obj roberta_embeddings_roberta_urdu_small\n",
            "nlu.load('ur.embed.urdu_vec_140M_300d') returns Spark NLP model_anno_obj urduvec_140M_300d\n",
            "nlu.load('ur.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <uz> NLU provides the following Models : \n",
            "nlu.load('uz.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <vec> NLU provides the following Models : \n",
            "nlu.load('vec.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <vi> NLU provides the following Models : \n",
            "nlu.load('vi.embed.bert.cased_base') returns Spark NLP model_anno_obj bert_embeddings_base_vi_cased\n",
            "nlu.load('vi.embed.bert_cased') returns Spark NLP model_anno_obj bert_embeddings_bert_base_vi_cased\n",
            "nlu.load('vi.embed.distilbert.cased') returns Spark NLP model_anno_obj distilbert_base_cased\n",
            "nlu.load('vi.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <vls> NLU provides the following Models : \n",
            "nlu.load('vls.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <vo> NLU provides the following Models : \n",
            "nlu.load('vo.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <wa> NLU provides the following Models : \n",
            "nlu.load('wa.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <war> NLU provides the following Models : \n",
            "nlu.load('war.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <wo> NLU provides the following Models : \n",
            "nlu.load('wo.embed.xlm_roberta') returns Spark NLP model_anno_obj xlm_roberta_base_finetuned_wolof\n",
            "For language <xmf> NLU provides the following Models : \n",
            "nlu.load('xmf.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <xx> NLU provides the following Models : \n",
            "nlu.load('xx.embed') returns Spark NLP model_anno_obj glove_840B_300\n",
            "nlu.load('xx.embed.albert.indic') returns Spark NLP model_anno_obj albert_indic\n",
            "nlu.load('xx.embed.bert') returns Spark NLP model_anno_obj bert_multi_cased\n",
            "nlu.load('xx.embed.bert.muril') returns Spark NLP model_anno_obj bert_muril\n",
            "nlu.load('xx.embed.bert_base_multilingual_cased') returns Spark NLP model_anno_obj bert_base_multilingual_cased\n",
            "nlu.load('xx.embed.bert_base_multilingual_uncased') returns Spark NLP model_anno_obj bert_base_multilingual_uncased\n",
            "nlu.load('xx.embed.bert_multi_cased') returns Spark NLP model_anno_obj bert_multi_cased\n",
            "nlu.load('xx.embed.distilbert') returns Spark NLP model_anno_obj distilbert_base_multilingual_cased\n",
            "nlu.load('xx.embed.glove.6B_300') returns Spark NLP model_anno_obj glove_6B_300\n",
            "nlu.load('xx.embed.glove.840B_300') returns Spark NLP model_anno_obj glove_840B_300\n",
            "nlu.load('xx.embed.glove.glove_6B_100') returns Spark NLP model_anno_obj glove_6B_100\n",
            "nlu.load('xx.embed.mdeberta_v3_base') returns Spark NLP model_anno_obj mdeberta_v3_base\n",
            "nlu.load('xx.embed.xlm') returns Spark NLP model_anno_obj xlm_roberta_base\n",
            "nlu.load('xx.embed.xlm.base') returns Spark NLP model_anno_obj xlm_roberta_base\n",
            "nlu.load('xx.embed.xlm.twitter') returns Spark NLP model_anno_obj twitter_xlm_roberta_base\n",
            "nlu.load('xx.embed.xlm_roberta_large') returns Spark NLP model_anno_obj xlm_roberta_large\n",
            "nlu.load('xx.embed.xlm_roberta_xtreme_base') returns Spark NLP model_anno_obj xlm_roberta_xtreme_base\n",
            "nlu.load('xx.embed.xlmr_roberta.base') returns Spark NLP model_anno_obj xlmroberta_embeddings_afriberta_base\n",
            "nlu.load('xx.embed.xlmr_roberta.large') returns Spark NLP model_anno_obj xlmroberta_embeddings_afriberta_large\n",
            "nlu.load('xx.embed.xlmr_roberta.large.by_hfl') returns Spark NLP model_anno_obj xlmroberta_embeddings_cino_large\n",
            "nlu.load('xx.embed.xlmr_roberta.large_128d') returns Spark NLP model_anno_obj xlmroberta_embeddings_roberta_large_eng_ara_128k\n",
            "nlu.load('xx.embed.xlmr_roberta.mini_lm_mini') returns Spark NLP model_anno_obj xlmroberta_embeddings_fairlex_fscs_minilm\n",
            "nlu.load('xx.embed.xlmr_roberta.small') returns Spark NLP model_anno_obj xlmroberta_embeddings_afriberta_small\n",
            "nlu.load('xx.embed.xlmr_roberta.v2_base') returns Spark NLP model_anno_obj xlmroberta_embeddings_cino_base_v2\n",
            "nlu.load('xx.embed.xlmr_roberta.v2_large') returns Spark NLP model_anno_obj xlmroberta_embeddings_cino_large_v2\n",
            "nlu.load('xx.embed.xlmr_roberta.v2_small') returns Spark NLP model_anno_obj xlmroberta_embeddings_cino_small_v2\n",
            "For language <yi> NLU provides the following Models : \n",
            "nlu.load('yi.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <yo> NLU provides the following Models : \n",
            "nlu.load('yo.embed.bert.cased_multilingual_base_finetuned') returns Spark NLP model_anno_obj bert_embeddings_base_multilingual_cased_finetuned_yoruba\n",
            "nlu.load('yo.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "nlu.load('yo.embed.xlm_roberta') returns Spark NLP model_anno_obj xlm_roberta_base_finetuned_yoruba\n",
            "For language <zea> NLU provides the following Models : \n",
            "nlu.load('zea.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "For language <zh> NLU provides the following Models : \n",
            "nlu.load('zh.embed') returns Spark NLP model_anno_obj bert_base_chinese\n",
            "nlu.load('zh.embed.bert') returns Spark NLP model_anno_obj bert_base_chinese\n",
            "nlu.load('zh.embed.bert.10l_128d_128d') returns Spark NLP model_anno_obj bert_embeddings_chinese_roberta_l_10_h_128\n",
            "nlu.load('zh.embed.bert.10l_256d_256d') returns Spark NLP model_anno_obj bert_embeddings_chinese_roberta_l_10_h_256\n",
            "nlu.load('zh.embed.bert.10l_512d_512d') returns Spark NLP model_anno_obj bert_embeddings_chinese_roberta_l_10_h_512\n",
            "nlu.load('zh.embed.bert.10l_768d_768d') returns Spark NLP model_anno_obj bert_embeddings_chinese_roberta_l_10_h_768\n",
            "nlu.load('zh.embed.bert.12l_128d_128d') returns Spark NLP model_anno_obj bert_embeddings_chinese_roberta_l_12_h_128\n",
            "nlu.load('zh.embed.bert.12l_256d_256d') returns Spark NLP model_anno_obj bert_embeddings_chinese_roberta_l_12_h_256\n",
            "nlu.load('zh.embed.bert.12l_512d_512d') returns Spark NLP model_anno_obj bert_embeddings_chinese_roberta_l_12_h_512\n",
            "nlu.load('zh.embed.bert.12l_768d_768d') returns Spark NLP model_anno_obj bert_embeddings_chinese_roberta_l_12_h_768\n",
            "nlu.load('zh.embed.bert.2l_128d_128d') returns Spark NLP model_anno_obj bert_embeddings_chinese_roberta_l_2_h_128\n",
            "nlu.load('zh.embed.bert.2l_256d_256d') returns Spark NLP model_anno_obj bert_embeddings_chinese_roberta_l_2_h_256\n",
            "nlu.load('zh.embed.bert.2l_512d_512d') returns Spark NLP model_anno_obj bert_embeddings_chinese_roberta_l_2_h_512\n",
            "nlu.load('zh.embed.bert.2l_768d_768d') returns Spark NLP model_anno_obj bert_embeddings_chinese_roberta_l_2_h_768\n",
            "nlu.load('zh.embed.bert.4l_128d_128d') returns Spark NLP model_anno_obj bert_embeddings_chinese_roberta_l_4_h_128\n",
            "nlu.load('zh.embed.bert.4l_256d_256d') returns Spark NLP model_anno_obj bert_embeddings_chinese_roberta_l_4_h_256\n",
            "nlu.load('zh.embed.bert.4l_512d_512d') returns Spark NLP model_anno_obj bert_embeddings_chinese_roberta_l_4_h_512\n",
            "nlu.load('zh.embed.bert.4l_768d_768d') returns Spark NLP model_anno_obj bert_embeddings_chinese_roberta_l_4_h_768\n",
            "nlu.load('zh.embed.bert.6l_128d_128d') returns Spark NLP model_anno_obj bert_embeddings_chinese_roberta_l_6_h_128\n",
            "nlu.load('zh.embed.bert.6l_256d_256d') returns Spark NLP model_anno_obj bert_embeddings_chinese_roberta_l_6_h_256\n",
            "nlu.load('zh.embed.bert.6l_512d_512d') returns Spark NLP model_anno_obj bert_embeddings_chinese_roberta_l_6_h_512\n",
            "nlu.load('zh.embed.bert.6l_768d_768d') returns Spark NLP model_anno_obj bert_embeddings_chinese_roberta_l_6_h_768\n",
            "nlu.load('zh.embed.bert.8l_128d_128d') returns Spark NLP model_anno_obj bert_embeddings_chinese_roberta_l_8_h_128\n",
            "nlu.load('zh.embed.bert.8l_256d_256d') returns Spark NLP model_anno_obj bert_embeddings_chinese_roberta_l_8_h_256\n",
            "nlu.load('zh.embed.bert.8l_512d_512d') returns Spark NLP model_anno_obj bert_embeddings_chinese_roberta_l_8_h_512\n",
            "nlu.load('zh.embed.bert.8l_768d_768d') returns Spark NLP model_anno_obj bert_embeddings_chinese_roberta_l_8_h_768\n",
            "nlu.load('zh.embed.bert.base') returns Spark NLP model_anno_obj bert_embeddings_base_chinese\n",
            "nlu.load('zh.embed.bert.base.by_model_attribution_challenge') returns Spark NLP model_anno_obj bert_embeddings_model_attribution_challenge_base_chinese\n",
            "nlu.load('zh.embed.bert.base.by_ptrsxu') returns Spark NLP model_anno_obj bert_embeddings_ptrsxu_base_chinese\n",
            "nlu.load('zh.embed.bert.by_ptrsxu') returns Spark NLP model_anno_obj bert_embeddings_ptrsxu_chinese_wwm_ext\n",
            "nlu.load('zh.embed.bert.by_qinluo') returns Spark NLP model_anno_obj bert_embeddings_wo_chinese_plus\n",
            "nlu.load('zh.embed.bert.cased_base') returns Spark NLP model_anno_obj bert_embeddings_base_zh_cased\n",
            "nlu.load('zh.embed.bert.chinese_wwm') returns Spark NLP model_anno_obj bert_embeddings_chinese_wwm\n",
            "nlu.load('zh.embed.bert.large') returns Spark NLP model_anno_obj bert_embeddings_chinese_lert_large\n",
            "nlu.load('zh.embed.bert.large.by_hfl') returns Spark NLP model_anno_obj bert_embeddings_chinese_mac_large\n",
            "nlu.load('zh.embed.bert.lert.base.by_hfl') returns Spark NLP model_anno_obj bert_embeddings_chinese_lert_base\n",
            "nlu.load('zh.embed.bert.mac.base.by_hfl') returns Spark NLP model_anno_obj bert_embeddings_chinese_mac_base\n",
            "nlu.load('zh.embed.bert.mini') returns Spark NLP model_anno_obj bert_embeddings_minirbt_h256\n",
            "nlu.load('zh.embed.bert.mini.by_hfl') returns Spark NLP model_anno_obj bert_embeddings_minirbt_h288\n",
            "nlu.load('zh.embed.bert.rbt4_h312.by_hfl') returns Spark NLP model_anno_obj bert_embeddings_rbt4_h312\n",
            "nlu.load('zh.embed.bert.small') returns Spark NLP model_anno_obj bert_embeddings_chinese_lert_small\n",
            "nlu.load('zh.embed.bert.wwm') returns Spark NLP model_anno_obj chinese_bert_wwm\n",
            "nlu.load('zh.embed.bert.wwm_ext.by_hfl') returns Spark NLP model_anno_obj bert_embeddings_hfl_chinese_wwm_ext\n",
            "nlu.load('zh.embed.bert_5lang_cased') returns Spark NLP model_anno_obj bert_embeddings_bert_base_5lang_cased\n",
            "nlu.load('zh.embed.bert_base_chinese_jinyong') returns Spark NLP model_anno_obj bert_embeddings_bert_base_chinese_jinyong\n",
            "nlu.load('zh.embed.bert_base_zh_cased') returns Spark NLP model_anno_obj bert_embeddings_bert_base_zh_cased\n",
            "nlu.load('zh.embed.bert_large_chinese') returns Spark NLP model_anno_obj bert_embeddings_bert_large_chinese\n",
            "nlu.load('zh.embed.chinese_bert_wwm_ext') returns Spark NLP model_anno_obj bert_embeddings_chinese_bert_wwm_ext\n",
            "nlu.load('zh.embed.chinese_macbert_base') returns Spark NLP model_anno_obj bert_embeddings_chinese_macbert_base\n",
            "nlu.load('zh.embed.chinese_macbert_large') returns Spark NLP model_anno_obj bert_embeddings_chinese_macbert_large\n",
            "nlu.load('zh.embed.chinese_roberta_wwm_ext') returns Spark NLP model_anno_obj bert_embeddings_chinese_roberta_wwm_ext\n",
            "nlu.load('zh.embed.chinese_roberta_wwm_ext_large') returns Spark NLP model_anno_obj bert_embeddings_chinese_roberta_wwm_ext_large\n",
            "nlu.load('zh.embed.chinese_roberta_wwm_large_ext_fix_mlm') returns Spark NLP model_anno_obj bert_embeddings_chinese_roberta_wwm_large_ext_fix_mlm\n",
            "nlu.load('zh.embed.distilbert_base_cased') returns Spark NLP model_anno_obj distilbert_embeddings_distilbert_base_zh_cased\n",
            "nlu.load('zh.embed.env_bert_chinese') returns Spark NLP model_anno_obj bert_embeddings_env_bert_chinese\n",
            "nlu.load('zh.embed.jdt_fin_roberta_wwm') returns Spark NLP model_anno_obj bert_embeddings_jdt_fin_roberta_wwm\n",
            "nlu.load('zh.embed.jdt_fin_roberta_wwm_large') returns Spark NLP model_anno_obj bert_embeddings_jdt_fin_roberta_wwm_large\n",
            "nlu.load('zh.embed.macbert4csc_base_chinese') returns Spark NLP model_anno_obj bert_embeddings_macbert4csc_base_chinese\n",
            "nlu.load('zh.embed.mengzi_bert_base') returns Spark NLP model_anno_obj bert_embeddings_mengzi_bert_base\n",
            "nlu.load('zh.embed.mengzi_bert_base_fin') returns Spark NLP model_anno_obj bert_embeddings_mengzi_bert_base_fin\n",
            "nlu.load('zh.embed.mengzi_oscar_base') returns Spark NLP model_anno_obj bert_embeddings_mengzi_oscar_base\n",
            "nlu.load('zh.embed.mengzi_oscar_base_caption') returns Spark NLP model_anno_obj bert_embeddings_mengzi_oscar_base_caption\n",
            "nlu.load('zh.embed.mengzi_oscar_base_retrieval') returns Spark NLP model_anno_obj bert_embeddings_mengzi_oscar_base_retrieval\n",
            "nlu.load('zh.embed.rbt3') returns Spark NLP model_anno_obj bert_embeddings_rbt3\n",
            "nlu.load('zh.embed.rbt4') returns Spark NLP model_anno_obj bert_embeddings_rbt4\n",
            "nlu.load('zh.embed.rbt6') returns Spark NLP model_anno_obj bert_embeddings_rbt6\n",
            "nlu.load('zh.embed.rbtl3') returns Spark NLP model_anno_obj bert_embeddings_rbtl3\n",
            "nlu.load('zh.embed.roberta.wwm_ext.by_hfl') returns Spark NLP model_anno_obj bert_embeddings_hfl_chinese_roberta_wwm_ext\n",
            "nlu.load('zh.embed.roberta_base_wechsel_chinese') returns Spark NLP model_anno_obj roberta_embeddings_roberta_base_wechsel_chinese\n",
            "nlu.load('zh.embed.sikubert') returns Spark NLP model_anno_obj bert_embeddings_sikubert\n",
            "nlu.load('zh.embed.sikuroberta') returns Spark NLP model_anno_obj bert_embeddings_sikuroberta\n",
            "nlu.load('zh.embed.uer_large') returns Spark NLP model_anno_obj bert_embeddings_uer_large\n",
            "nlu.load('zh.embed.w2v_cc_300d') returns Spark NLP model_anno_obj w2v_cc_300d\n",
            "nlu.load('zh.embed.wobert_chinese_base') returns Spark NLP model_anno_obj bert_embeddings_wobert_chinese_base\n",
            "nlu.load('zh.embed.wobert_chinese_plus') returns Spark NLP model_anno_obj bert_embeddings_wobert_chinese_plus\n",
            "nlu.load('zh.embed.wobert_chinese_plus_base') returns Spark NLP model_anno_obj bert_embeddings_wobert_chinese_plus_base\n",
            "nlu.load('zh.embed.xlmr_roberta.mini_lm_mini') returns Spark NLP model_anno_obj xlmroberta_embeddings_fairlex_cail_minilm\n",
            "nlu.load('zh.embed.xlnet') returns Spark NLP model_anno_obj chinese_xlnet_base\n",
            "For language <zu> NLU provides the following Models : \n",
            "nlu.load('zu.embed.roberta') returns Spark NLP model_anno_obj roberta_embeddings_zuberta\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "Xz7xnvbCFxE3",
        "outputId": "39864822-1fde-4fbf-c44c-d15e62d9707a"
      },
      "source": [
        "# Add bert word embeddings to pipe\n",
        "fitted_pipe = nlp.load('bert train.ner').fit(dataset_path=train_path)\n",
        "\n",
        "# predict with the trainable pipeline on dataset and get predictions\n",
        "preds = fitted_pipe.predict('Donald Trump and Angela Merkel dont share many oppinions')\n",
        "preds"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Warning::Spark Session already created, some configs may not take.\n",
            "Warning::Spark Session already created, some configs may not take.\n",
            "small_bert_L2_128 download started this may take some time.\n",
            "Approximate size to download 16.1 MB\n",
            "[OK!]\n",
            "sentence_detector_dl download started this may take some time.\n",
            "Approximate size to download 354.6 KB\n",
            "[OK!]\n",
            "Warning::Spark Session already created, some configs may not take.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                            document        entities_ner  \\\n",
              "0  Donald Trump and Angela Merkel dont share many...        Donald Trump   \n",
              "0  Donald Trump and Angela Merkel dont share many...  Angela Merkel dont   \n",
              "\n",
              "  entities_ner_class entities_ner_confidence entities_ner_origin_chunk  \\\n",
              "0                PER                  0.9427                         0   \n",
              "0                PER               0.9236667                         1   \n",
              "\n",
              "  entities_ner_origin_sentence  \\\n",
              "0                            0   \n",
              "0                            0   \n",
              "\n",
              "                                 word_embedding_bert  \n",
              "0  [[-0.44760167598724365, 1.0348622798919678, 0....  \n",
              "0  [[-0.44760167598724365, 1.0348622798919678, 0....  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-da160d0f-fc90-462b-b91f-f7b5d370720c\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>document</th>\n",
              "      <th>entities_ner</th>\n",
              "      <th>entities_ner_class</th>\n",
              "      <th>entities_ner_confidence</th>\n",
              "      <th>entities_ner_origin_chunk</th>\n",
              "      <th>entities_ner_origin_sentence</th>\n",
              "      <th>word_embedding_bert</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Donald Trump and Angela Merkel dont share many...</td>\n",
              "      <td>Donald Trump</td>\n",
              "      <td>PER</td>\n",
              "      <td>0.9427</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>[[-0.44760167598724365, 1.0348622798919678, 0....</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Donald Trump and Angela Merkel dont share many...</td>\n",
              "      <td>Angela Merkel dont</td>\n",
              "      <td>PER</td>\n",
              "      <td>0.9236667</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>[[-0.44760167598724365, 1.0348622798919678, 0....</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-da160d0f-fc90-462b-b91f-f7b5d370720c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-da160d0f-fc90-462b-b91f-f7b5d370720c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-da160d0f-fc90-462b-b91f-f7b5d370720c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-874735c6-ddbb-4984-9ed7-4065e2a39f65\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-874735c6-ddbb-4984-9ed7-4065e2a39f65')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-874735c6-ddbb-4984-9ed7-4065e2a39f65 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2BB-NwZUoHSe"
      },
      "source": [
        "# 5. Lets save the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eLex095goHwm"
      },
      "source": [
        "stored_model_path = './models/classifier_dl_trained'\n",
        "fitted_pipe.save(stored_model_path)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e_b2DPd4rCiU"
      },
      "source": [
        "# 6. Lets load the model from HDD.\n",
        "This makes Offlien NLU usage possible!   \n",
        "You need to call nlu.load(path=path_to_the_pipe) to load a model/pipeline from disk."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SO4uz45MoRgp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        },
        "outputId": "6987b2bc-9d70-4082-b8b5-915477ecb6e7"
      },
      "source": [
        "hdd_pipe = nlp.load(path=stored_model_path)\n",
        "\n",
        "preds = hdd_pipe.predict('Donald Trump and Angela Merkel dont share many oppinions on laws about cheeseburgers')\n",
        "preds"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Warning::Spark Session already created, some configs may not take.\n",
            "Warning::Spark Session already created, some configs may not take.\n",
            "Warning::Spark Session already created, some configs may not take.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                            document  entities_from_disk  \\\n",
              "0  Donald Trump and Angela Merkel dont share many...        Donald Trump   \n",
              "0  Donald Trump and Angela Merkel dont share many...  Angela Merkel dont   \n",
              "\n",
              "  entities_from_disk_class entities_from_disk_confidence  \\\n",
              "0                      PER                        0.9282   \n",
              "0                      PER                        0.8248   \n",
              "\n",
              "  entities_from_disk_origin_chunk entities_from_disk_origin_sentence  \\\n",
              "0                               0                                  0   \n",
              "0                               1                                  0   \n",
              "\n",
              "                            word_embedding_from_disk  \n",
              "0  [[-0.687057375907898, 1.1118954420089722, 0.58...  \n",
              "0  [[-0.687057375907898, 1.1118954420089722, 0.58...  "
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "      <th></th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Donald Trump and Angela Merkel dont share many...</td>\n",
              "      <td>Donald Trump</td>\n",
              "      <td>PER</td>\n",
              "      <td>0.9282</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>[[-0.687057375907898, 1.1118954420089722, 0.58...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Donald Trump and Angela Merkel dont share many...</td>\n",
              "      <td>Angela Merkel dont</td>\n",
              "      <td>PER</td>\n",
              "      <td>0.8248</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>[[-0.687057375907898, 1.1118954420089722, 0.58...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ce0c21ba-4067-48e3-a2e1-27e3ef92f7ba')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ce0c21ba-4067-48e3-a2e1-27e3ef92f7ba button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ce0c21ba-4067-48e3-a2e1-27e3ef92f7ba');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
              "<div id=\"df-11fbee42-9741-41fa-9933-8c813caa7009\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-11fbee42-9741-41fa-9933-8c813caa7009')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-11fbee42-9741-41fa-9933-8c813caa7009 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e0CVlkk9v6Qi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "be0a118f-72a6-4cd7-9968-2f644036f4fa"
      },
      "source": [
        "hdd_pipe.print_info()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",
            ">>> component_list['document_assembler'] has settable params:\n",
            "component_list['document_assembler'].setCleanupMode('shrink')  | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : shrink\n",
            ">>> component_list['sentence_detector_dl'] has settable params:\n",
            "component_list['sentence_detector_dl'].setCustomBounds([])  | Info: characters used to explicitly mark sentence bounds | Currently set to : []\n",
            "component_list['sentence_detector_dl'].setExplodeSentences(False)  | Info: whether to explode each sentence into a different row, for better parallelization. Defaults to false. | Currently set to : False\n",
            "component_list['sentence_detector_dl'].setMaxLength(99999)  | Info: Set the maximum allowed length for each sentence | Currently set to : 99999\n",
            "component_list['sentence_detector_dl'].setMinLength(0)     | Info: Set the minimum allowed length for each sentence. | Currently set to : 0\n",
            "component_list['sentence_detector_dl'].setUseCustomBoundsOnly(False)  | Info: Only utilize custom bounds in sentence detection | Currently set to : False\n",
            "component_list['sentence_detector_dl'].setEngine('tensorflow')  | Info: Deep Learning engine used for this model | Currently set to : tensorflow\n",
            "component_list['sentence_detector_dl'].setSplitLength(2147483647)  | Info: length at which sentences will be forcibly split. | Currently set to : 2147483647\n",
            "component_list['sentence_detector_dl'].setStorageRef('SentenceDetectorDLModel_c83c27f46b97')  | Info: storage unique identifier | Currently set to : SentenceDetectorDLModel_c83c27f46b97\n",
            "component_list['sentence_detector_dl'].setEncoder(com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@3d7fecba)  | Info: Data encoder | Currently set to : com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@3d7fecba\n",
            "component_list['sentence_detector_dl'].setImpossiblePenultimates(['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e'])  | Info: Impossible penultimates - list of strings which a sentence can't end with | Currently set to : ['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']\n",
            "component_list['sentence_detector_dl'].setModelArchitecture('cnn')  | Info: Model architecture (CNN) | Currently set to : cnn\n",
            ">>> component_list['tokenizer'] has settable params:\n",
            "component_list['tokenizer'].setCaseSensitiveExceptions(True)  | Info: Whether to care for case sensitiveness in exceptions | Currently set to : True\n",
            "component_list['tokenizer'].setTargetPattern('\\S+')        | Info: pattern to grab from text as token candidates. Defaults \\S+ | Currently set to : \\S+\n",
            "component_list['tokenizer'].setMaxLength(99999)            | Info: Set the maximum allowed length for each token | Currently set to : 99999\n",
            "component_list['tokenizer'].setMinLength(0)                | Info: Set the minimum allowed length for each token | Currently set to : 0\n",
            ">>> component_list['bert_embeddings@small_bert_L2_128'] has settable params:\n",
            "component_list['bert_embeddings@small_bert_L2_128'].setBatchSize(8)  | Info: Size of every batch | Currently set to : 8\n",
            "component_list['bert_embeddings@small_bert_L2_128'].setCaseSensitive(False)  | Info: whether to ignore case in tokens for embeddings matching | Currently set to : False\n",
            "component_list['bert_embeddings@small_bert_L2_128'].setDimension(128)  | Info: Number of embedding dimensions | Currently set to : 128\n",
            "component_list['bert_embeddings@small_bert_L2_128'].setMaxSentenceLength(128)  | Info: Max sentence length to process | Currently set to : 128\n",
            "component_list['bert_embeddings@small_bert_L2_128'].setEngine('tensorflow')  | Info: Deep Learning engine used for this model | Currently set to : tensorflow\n",
            "component_list['bert_embeddings@small_bert_L2_128'].setStorageRef('small_bert_L2_128')  | Info: unique reference name for identification | Currently set to : small_bert_L2_128\n",
            ">>> component_list['ner_dl@small_bert_L2_128'] has settable params:\n",
            "component_list['ner_dl@small_bert_L2_128'].setBatchSize(8)  | Info: Size of every batch | Currently set to : 8\n",
            "component_list['ner_dl@small_bert_L2_128'].setIncludeAllConfidenceScores(False)  | Info: whether to include all confidence scores in annotation metadata or just the score of the predicted tag | Currently set to : False\n",
            "component_list['ner_dl@small_bert_L2_128'].setIncludeConfidence(True)  | Info: whether to include confidence scores in annotation metadata | Currently set to : True\n",
            "component_list['ner_dl@small_bert_L2_128'].setEngine('tensorflow')  | Info: Deep Learning engine used for this model | Currently set to : tensorflow\n",
            "component_list['ner_dl@small_bert_L2_128'].setClasses(['O', 'B-ORG', 'I-ORG', 'I-MISC', 'I-PER', 'B-LOC', 'B-MISC', 'I-LOC'])  | Info: get the tags used to trained this NerDLModel | Currently set to : ['O', 'B-ORG', 'I-ORG', 'I-MISC', 'I-PER', 'B-LOC', 'B-MISC', 'I-LOC']\n",
            "component_list['ner_dl@small_bert_L2_128'].setStorageRef('small_bert_L2_128')  | Info: unique reference name for identification | Currently set to : small_bert_L2_128\n",
            ">>> component_list['ner_converter'] has settable params:\n",
            "component_list['ner_converter'].setNerHasNoSchema(False)   | Info: set this to true if your NER tags coming from a model that does not have a IOB/IOB2 schema | Currently set to : False\n",
            "component_list['ner_converter'].setPreservePosition(True)  | Info: Whether to preserve the original position of the tokens in the original document or use the modified tokens | Currently set to : True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "USD6d66Sw6_P"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    }
  ]
}